Right-Wing Authoritarianism and Social Dominance Orientation as Related to Discrimination: The Mediating Role of Moral Exclusion
Hadi Sam Nariman
June 08, 2018
Required Packages
library(foreign)
library(ggpubr)
library(MVN)
library(lavaan)
library(tidyverse)
library(semPlot)
library(car)
library(moments)
library(dplyr)
library(psych)Reading the Dataset
MOREX = read.table("C:\\Users\\User\\Desktop\\R-PROJECT\\MEX_R.dat", header=TRUE)
View (MOREX)
head(MOREX)## NEG1 NEG2 NEG3 NEG5 NEGn DISC MIN MEX SDO RWA sdo1 sdo2
## 1 2 3 4 2 6 3.4 4.000000 4.000000 3.250 4.333333 1 2
## 2 1 1 1 1 1 1.0 6.333333 1.666667 1.000 1.333333 1 1
## 3 1 1 5 1 1 1.8 3.666667 4.333333 1.625 1.333333 1 1
## 4 3 3 3 3 4 3.2 4.333333 3.666667 2.500 2.166667 1 1
## 5 1 2 2 7 4 3.2 3.000000 5.000000 1.000 1.000000 1 1
## 6 6 7 6 1 4 4.8 2.666667 5.333333 1.625 2.666667 2 3
## sdo3 sdo4 sdo5 sdo6 sdo7 sdo8 MEX1 MEX2 MEX3 rwa1 rwa2 rwa3 rwa4 rwa5
## 1 5 4 5 6 1 2 3 4 5 2 5 4 4 5
## 2 1 1 1 1 1 1 1 1 3 3 1 1 1 1
## 3 2 1 2 3 1 2 1 6 6 2 1 1 1 1
## 4 1 1 6 6 2 2 2 4 5 3 2 2 2 2
## 5 1 1 1 1 1 1 1 7 7 1 1 1 1 1
## 6 3 1 1 1 1 1 2 7 7 2 4 4 1 1
## rwa6 age gender SDOI SDOII SDOIII SDOIV
## 1 6 60 2 1.5 4.5 5.5 1.5
## 2 1 69 2 1.0 1.0 1.0 1.0
## 3 2 51 2 1.0 1.5 2.5 1.5
## 4 2 66 2 1.0 1.0 6.0 2.0
## 5 1 65 2 1.0 1.0 1.0 1.0
## 6 4 42 2 2.5 2.0 1.0 1.0nrow(MOREX)## [1] 1015Executive Summary
Exploratory data analyses were performed and the study’s hypotheses were tested by performing two stractural equation models. Exploratory data analyses contained checking for missing values, exploring the nature of the variables by summarizing the data, checking for the outliers, checking for normality of the variables, checking for the reliability of the scales, and performing correlation analyses. Correlational analyses were run between items of Discriminatory Intergroup Attitude Scale, Moral Exclusion Scale, Right-Wing Authoritarianism Scale, and Social Dominance Orientation Scale. By the first hypothesis we assumed that there will be a significant and positive association between Right-Wing Authoritarianism as well as Social Dominance Orientation and discriminatory attitudes against the Roma people. The hypothesis was supported, as it was found that both Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) significantly and positively predicted negative intergroup attitudes against the Roma people. The second hypothesis was that the aformentioned association will be fully explained by moral exclusion. Our Second hypothesis was partially supported and it was found that SDO and RWA partially mediated the effects.
Description of the Dataset
In an online survey study, 1015 Hungarian participants (\(M_{age}\) = 43.9, \(SD\) = 14.18; 523 female, 492 male) were recruited a set of 7-point likert type scales. The questionnaire measured Right-Wing Authoritarianism (RWA, hereafter), Social Dominance Orientation (SDO, hereafter), Moral Exclusion (MEX, hereafter), and five items measuring discriminatory attitudes against the Roma people residing in Hungary.
The dataset was originally in an SPSS file which was later converted to a dat file, and finally read by R.
The dataset belongs to an actual research which is aimed to be submitted as a manuscript to a scientific journal in the near future. Thus, it is supposed to be treated confidentially.
Exploratory Data Analyses
Exploring Missing Variables in the Dataset
sapply(MOREX,function(x) sum(is.na(x)))## NEG1 NEG2 NEG3 NEG5 NEGn DISC MIN MEX SDO RWA
## 0 0 0 0 0 0 0 0 0 0
## sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8 MEX1 MEX2
## 0 0 0 0 0 0 0 0 0 0
## MEX3 rwa1 rwa2 rwa3 rwa4 rwa5 rwa6 age gender SDOI
## 0 0 0 0 0 0 0 0 0 0
## SDOII SDOIII SDOIV
## 0 0 0Exploring the Nature of the Variables
MOREX%>%
select(-gender, -age)%>%
summary()## NEG1 NEG2 NEG3 NEG5
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.00 Median :4.000 Median :4.000 Median :2.000
## Mean :2.73 Mean :3.828 Mean :3.866 Mean :2.903
## 3rd Qu.:4.00 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:4.000
## Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.000
## NEGn DISC MIN MEX
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.00 1st Qu.:3.000 1st Qu.:3.000
## Median :2.000 Median :3.00 Median :4.000 Median :4.000
## Mean :2.725 Mean :3.21 Mean :3.931 Mean :4.069
## 3rd Qu.:4.000 3rd Qu.:4.20 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000
## SDO RWA sdo1 sdo2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.750 1st Qu.:1.667 1st Qu.:1.000 1st Qu.:1.000
## Median :2.750 Median :2.667 Median :2.000 Median :1.000
## Mean :2.822 Mean :2.799 Mean :2.761 Mean :2.243
## 3rd Qu.:3.750 3rd Qu.:3.667 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :7.000 Max. :6.833 Max. :7.000 Max. :7.000
## sdo3 sdo4 sdo5 sdo6
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
## Median :3.000 Median :1.000 Median :3.000 Median :4.000
## Mean :3.062 Mean :2.527 Mean :3.369 Mean :4.307
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## sdo7 sdo8 MEX1 MEX2
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000
## Median :1.000 Median :2.000 Median :1.000 Median :5.000
## Mean :1.865 Mean :2.437 Mean :2.229 Mean :4.655
## 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## MEX3 rwa1 rwa2 rwa3
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :6.000 Median :2.000 Median :3.000 Median :2.000
## Mean :5.322 Mean :2.815 Mean :3.448 Mean :2.809
## 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.:6.000 3rd Qu.:4.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## rwa4 rwa5 rwa6 SDOI
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :1.000 Median :2.000
## Mean :2.556 Mean :2.659 Mean :2.507 Mean :2.502
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.500
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## SDOII SDOIII SDOIV
## Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:2.500 1st Qu.:1.000
## Median :2.500 Median :4.000 Median :1.500
## Mean :2.795 Mean :3.838 Mean :2.151
## 3rd Qu.:4.000 3rd Qu.:5.500 3rd Qu.:3.000
## Max. :7.000 Max. :7.000 Max. :7.000MOREX%>%
select(-gender)%>%
describe()## vars n mean sd median trimmed mad min max range skew
## NEG1 1 1015 2.73 2.02 2.00 2.43 1.48 1 7.00 6.00 0.88
## NEG2 2 1015 3.83 2.37 4.00 3.78 4.45 1 7.00 6.00 0.09
## NEG3 3 1015 3.87 2.04 4.00 3.83 2.97 1 7.00 6.00 0.11
## NEG5 4 1015 2.90 2.00 2.00 2.64 1.48 1 7.00 6.00 0.77
## NEGn 5 1015 2.73 1.83 2.00 2.45 1.48 1 7.00 6.00 0.87
## DISC 6 1015 3.21 1.53 3.00 3.12 1.78 1 7.00 6.00 0.42
## MIN 7 1015 3.93 1.56 4.00 3.90 1.48 1 7.00 6.00 0.18
## MEX 8 1015 4.07 1.56 4.00 4.10 1.48 1 7.00 6.00 -0.18
## SDO 9 1015 2.82 1.23 2.75 2.76 1.48 1 7.00 6.00 0.39
## RWA 10 1015 2.80 1.32 2.67 2.72 1.48 1 6.83 5.83 0.45
## sdo1 11 1015 2.76 1.90 2.00 2.51 1.48 1 7.00 6.00 0.71
## sdo2 12 1015 2.24 1.87 1.00 1.86 0.00 1 7.00 6.00 1.32
## sdo3 13 1015 3.06 2.09 3.00 2.83 2.97 1 7.00 6.00 0.56
## sdo4 14 1015 2.53 2.00 1.00 2.18 0.00 1 7.00 6.00 1.06
## sdo5 15 1015 3.37 2.16 3.00 3.21 2.97 1 7.00 6.00 0.37
## sdo6 16 1015 4.31 2.22 4.00 4.38 2.97 1 7.00 6.00 -0.22
## sdo7 17 1015 1.87 1.50 1.00 1.52 0.00 1 7.00 6.00 1.89
## sdo8 18 1015 2.44 1.76 2.00 2.14 1.48 1 7.00 6.00 1.09
## MEX1 19 1015 2.23 1.66 1.00 1.91 0.00 1 7.00 6.00 1.32
## MEX2 20 1015 4.66 2.01 5.00 4.81 2.97 1 7.00 6.00 -0.33
## MEX3 21 1015 5.32 1.89 6.00 5.60 1.48 1 7.00 6.00 -0.82
## rwa1 22 1015 2.81 1.82 2.00 2.59 1.48 1 7.00 6.00 0.66
## rwa2 23 1015 3.45 2.27 3.00 3.31 2.97 1 7.00 6.00 0.35
## rwa3 24 1015 2.81 1.88 2.00 2.57 1.48 1 7.00 6.00 0.73
## rwa4 25 1015 2.56 1.84 2.00 2.27 1.48 1 7.00 6.00 0.90
## rwa5 26 1015 2.66 1.96 2.00 2.37 1.48 1 7.00 6.00 0.87
## rwa6 27 1015 2.51 1.88 1.00 2.20 0.00 1 7.00 6.00 0.95
## age 28 1015 43.92 14.18 43.00 43.97 17.79 18 69.00 51.00 -0.01
## SDOI 29 1015 2.50 1.60 2.00 2.27 1.48 1 7.00 6.00 0.94
## SDOII 30 1015 2.79 1.76 2.50 2.58 2.22 1 7.00 6.00 0.69
## SDOIII 31 1015 3.84 1.90 4.00 3.80 2.22 1 7.00 6.00 0.02
## SDOIV 32 1015 2.15 1.48 1.50 1.89 0.74 1 7.00 6.00 1.39
## kurtosis se
## NEG1 -0.52 0.06
## NEG2 -1.55 0.07
## NEG3 -1.17 0.06
## NEG5 -0.65 0.06
## NEGn -0.20 0.06
## DISC -0.58 0.05
## MIN -0.76 0.05
## MEX -0.76 0.05
## SDO -0.46 0.04
## RWA -0.59 0.04
## sdo1 -0.68 0.06
## sdo2 0.45 0.06
## sdo3 -1.03 0.07
## sdo4 -0.21 0.06
## sdo5 -1.27 0.07
## sdo6 -1.36 0.07
## sdo7 2.89 0.05
## sdo8 0.17 0.06
## MEX1 0.90 0.05
## MEX2 -1.12 0.06
## MEX3 -0.48 0.06
## rwa1 -0.66 0.06
## rwa2 -1.38 0.07
## rwa3 -0.65 0.06
## rwa4 -0.31 0.06
## rwa5 -0.55 0.06
## rwa6 -0.37 0.06
## age -1.16 0.45
## SDOI 0.08 0.05
## SDOII -0.51 0.06
## SDOIII -1.04 0.06
## SDOIV 1.37 0.05table(MOREX$gender)##
## 1 2
## 492 523prop.table(table(MOREX$gender))##
## 1 2
## 0.4847291 0.5152709Reliability Analysis
tbl_df(MOREX)## # A tibble: 1,015 x 33
## NEG1 NEG2 NEG3 NEG5 NEGn DISC MIN MEX SDO RWA sdo1 sdo2
## <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
## 1 2 3 4 2 6 3.4 4 4 3.25 4.33 1 2
## 2 1 1 1 1 1 1 6.33 1.67 1 1.33 1 1
## 3 1 1 5 1 1 1.8 3.67 4.33 1.62 1.33 1 1
## 4 3 3 3 3 4 3.2 4.33 3.67 2.5 2.17 1 1
## 5 1 2 2 7 4 3.2 3 5 1 1 1 1
## 6 6 7 6 1 4 4.8 2.67 5.33 1.62 2.67 2 3
## 7 1 1 1 1 1 1 7 1 1 1 1 1
## 8 1 1 6 2 4 2.8 7 1 1.38 2.17 1 1
## 9 1 1 2 6 5 3 4.67 3.33 2.12 1 2 1
## 10 1 1 1 1 1 1 7 1 1 1 1 1
## # ... with 1,005 more rows, and 21 more variables: sdo3 <int>, sdo4 <int>,
## # sdo5 <int>, sdo6 <int>, sdo7 <int>, sdo8 <int>, MEX1 <int>,
## # MEX2 <int>, MEX3 <int>, rwa1 <int>, rwa2 <int>, rwa3 <int>,
## # rwa4 <int>, rwa5 <int>, rwa6 <int>, age <int>, gender <int>,
## # SDOI <dbl>, SDOII <dbl>, SDOIII <dbl>, SDOIV <dbl>SOCIALDO <- select(MOREX, 11, 12, 13, 14, 15, 16, 17, 18)
alpha(SOCIALDO)##
## Reliability analysis
## Call: alpha(x = SOCIALDO)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.79 0.8 0.31 3.7 0.01 2.8 1.2 0.3
##
## lower alpha upper 95% confidence boundaries
## 0.76 0.78 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## sdo1 0.75 0.75 0.77 0.30 3.1 0.012 0.0173 0.29
## sdo2 0.77 0.77 0.78 0.32 3.3 0.011 0.0183 0.33
## sdo3 0.75 0.75 0.76 0.30 3.0 0.012 0.0175 0.30
## sdo4 0.76 0.76 0.78 0.31 3.2 0.011 0.0176 0.30
## sdo5 0.75 0.76 0.77 0.31 3.2 0.012 0.0159 0.30
## sdo6 0.77 0.77 0.78 0.33 3.4 0.011 0.0129 0.30
## sdo7 0.78 0.78 0.77 0.33 3.5 0.011 0.0071 0.33
## sdo8 0.75 0.75 0.74 0.29 2.9 0.012 0.0135 0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## sdo1 1015 0.67 0.67 0.61 0.55 2.8 1.9
## sdo2 1015 0.59 0.60 0.50 0.45 2.2 1.9
## sdo3 1015 0.69 0.69 0.63 0.56 3.1 2.1
## sdo4 1015 0.63 0.63 0.55 0.48 2.5 2.0
## sdo5 1015 0.67 0.64 0.57 0.52 3.4 2.2
## sdo6 1015 0.61 0.57 0.49 0.43 4.3 2.2
## sdo7 1015 0.50 0.55 0.49 0.37 1.9 1.5
## sdo8 1015 0.68 0.71 0.69 0.56 2.4 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## sdo1 0.42 0.11 0.11 0.16 0.10 0.04 0.06 0
## sdo2 0.61 0.08 0.08 0.08 0.05 0.04 0.06 0
## sdo3 0.38 0.11 0.09 0.14 0.11 0.06 0.10 0
## sdo4 0.52 0.12 0.08 0.10 0.07 0.04 0.08 0
## sdo5 0.32 0.13 0.08 0.16 0.09 0.09 0.13 0
## sdo6 0.19 0.08 0.08 0.18 0.10 0.12 0.26 0
## sdo7 0.65 0.13 0.07 0.07 0.03 0.01 0.03 0
## sdo8 0.47 0.16 0.11 0.13 0.05 0.04 0.05 0DISCRIMINATION <- select(MOREX, 1, 2, 3, 4, 5)
alpha(DISCRIMINATION)##
## Reliability analysis
## Call: alpha(x = DISCRIMINATION)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.78 0.44 4 0.01 3.2 1.5 0.43
##
## lower alpha upper 95% confidence boundaries
## 0.78 0.8 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## NEG1 0.75 0.76 0.71 0.44 3.2 0.013 0.0073 0.43
## NEG2 0.76 0.77 0.72 0.45 3.3 0.012 0.0066 0.45
## NEG3 0.78 0.79 0.75 0.48 3.7 0.011 0.0056 0.45
## NEG5 0.74 0.74 0.70 0.42 2.9 0.013 0.0076 0.42
## NEGn 0.75 0.75 0.71 0.43 3.0 0.013 0.0081 0.44
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## NEG1 1015 0.75 0.75 0.67 0.60 2.7 2.0
## NEG2 1015 0.76 0.73 0.64 0.57 3.8 2.4
## NEG3 1015 0.68 0.68 0.55 0.49 3.9 2.0
## NEG5 1015 0.78 0.79 0.72 0.64 2.9 2.0
## NEGn 1015 0.75 0.77 0.70 0.61 2.7 1.8
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## NEG1 0.44 0.13 0.09 0.14 0.06 0.05 0.09 0
## NEG2 0.30 0.08 0.07 0.13 0.09 0.09 0.23 0
## NEG3 0.19 0.11 0.14 0.21 0.10 0.10 0.16 0
## NEG5 0.36 0.18 0.11 0.13 0.07 0.05 0.09 0
## NEGn 0.37 0.18 0.11 0.20 0.04 0.03 0.07 0RIGHTWA <- select(MOREX, 22, 23, 24, 25, 26, 27)
alpha(RIGHTWA)##
## Reliability analysis
## Call: alpha(x = RIGHTWA)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.77 0.35 3.2 0.012 2.8 1.3 0.3
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.76 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## rwa1 0.73 0.73 0.73 0.35 2.7 0.013 0.020 0.31
## rwa2 0.72 0.72 0.69 0.34 2.5 0.014 0.010 0.31
## rwa3 0.71 0.72 0.69 0.34 2.6 0.014 0.010 0.31
## rwa4 0.73 0.73 0.73 0.35 2.7 0.013 0.021 0.31
## rwa5 0.70 0.70 0.70 0.32 2.4 0.015 0.020 0.28
## rwa6 0.75 0.75 0.76 0.38 3.1 0.012 0.022 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## rwa1 1015 0.65 0.66 0.55 0.48 2.8 1.8
## rwa2 1015 0.73 0.70 0.66 0.54 3.4 2.3
## rwa3 1015 0.71 0.70 0.65 0.55 2.8 1.9
## rwa4 1015 0.65 0.66 0.55 0.48 2.6 1.8
## rwa5 1015 0.74 0.74 0.68 0.59 2.7 2.0
## rwa6 1015 0.59 0.59 0.44 0.39 2.5 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## rwa1 0.37 0.15 0.10 0.20 0.07 0.06 0.05 0
## rwa2 0.33 0.12 0.08 0.14 0.07 0.09 0.17 0
## rwa3 0.37 0.17 0.11 0.16 0.07 0.07 0.05 0
## rwa4 0.48 0.11 0.08 0.19 0.05 0.04 0.05 0
## rwa5 0.46 0.13 0.09 0.13 0.07 0.06 0.06 0
## rwa6 0.51 0.11 0.07 0.15 0.07 0.05 0.05 0MORALEX <- select(MOREX, 19, 20, 21)
alpha(MORALEX)##
## Reliability analysis
## Call: alpha(x = MORALEX)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.75 0.54 3.5 0.012 4.1 1.6 0.46
##
## lower alpha upper 95% confidence boundaries
## 0.76 0.78 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## MEX1 0.86 0.86 0.76 0.76 6.4 0.0085 NA 0.76
## MEX2 0.56 0.57 0.40 0.40 1.3 0.0272 NA 0.40
## MEX3 0.63 0.63 0.46 0.46 1.7 0.0230 NA 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## MEX1 1015 0.72 0.74 0.50 0.46 2.2 1.7
## MEX2 1015 0.90 0.89 0.85 0.74 4.7 2.0
## MEX3 1015 0.87 0.86 0.81 0.69 5.3 1.9
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## MEX1 0.53 0.14 0.11 0.12 0.04 0.02 0.04 0
## MEX2 0.10 0.08 0.12 0.19 0.11 0.13 0.29 0
## MEX3 0.06 0.04 0.08 0.17 0.10 0.11 0.44 0Check for outliers
Boxplots
MOREX %>%
gather(variable, value, -gender, -age) %>%
ggplot() +
aes(x = fct_rev(variable), y = value) +
geom_boxplot() +
coord_flip()
Inquring into sdo2
OutlierS were prefered to be kept in the dataset.
outlierKD <- function(dt, var) {
var_name <- eval(substitute(var),eval(dt))
na1 <- sum(is.na(var_name))
m1 <- mean(var_name, na.rm = T)
par(mfrow=c(2, 2), oma=c(0,0,3,0))
boxplot(var_name, main="With outliers")
hist(var_name, main="With outliers", xlab=NA, ylab=NA)
outlier <- boxplot.stats(var_name)$out
mo <- mean(outlier)
var_name <- ifelse(var_name %in% outlier, NA, var_name)
boxplot(var_name, main="Without outliers")
hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
title("Outlier Check", outer=TRUE)
na2 <- sum(is.na(var_name))
cat("Outliers identified:", na2 - na1, "n")
cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n")
cat("Mean of the outliers:", round(mo, 2), "n")
m2 <- mean(var_name, na.rm = T)
cat("Mean without removing outliers:", round(m1, 2), "n")
cat("Mean if we remove outliers:", round(m2, 2), "n")
response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
if(response == "y" | response == "yes"){
dt[as.character(substitute(var))] <- invisible(var_name)
assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
cat("Outliers successfully removed", "n")
return(invisible(dt))
} else{
cat("Nothing changed", "n")
return(invisible(var_name)) }}
outlierKD(MOREX, sdo2)
## Outliers identified: 58 nPropotion (%) of outliers: 6.1 nMean of the outliers: 7 nMean without removing outliers: 2.24 nMean if we remove outliers: 1.96 nDo you want to remove outliers and to replace with NA? [yes/no]:
## Nothing changed nInquring into sdo7
OutlierS were prefered to be kept in the dataset.
outlierKD <- function(dt, var) {
var_name <- eval(substitute(var),eval(dt))
na1 <- sum(is.na(var_name))
m1 <- mean(var_name, na.rm = T)
par(mfrow=c(2, 2), oma=c(0,0,3,0))
boxplot(var_name, main="With outliers")
hist(var_name, main="With outliers", xlab=NA, ylab=NA)
outlier <- boxplot.stats(var_name)$out
mo <- mean(outlier)
var_name <- ifelse(var_name %in% outlier, NA, var_name)
boxplot(var_name, main="Without outliers")
hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
title("Outlier Check", outer=TRUE)
na2 <- sum(is.na(var_name))
cat("Outliers identified:", na2 - na1, "n")
cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n")
cat("Mean of the outliers:", round(mo, 2), "n")
m2 <- mean(var_name, na.rm = T)
cat("Mean without removing outliers:", round(m1, 2), "n")
cat("Mean if we remove outliers:", round(m2, 2), "n")
response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
if(response == "y" | response == "yes"){
dt[as.character(substitute(var))] <- invisible(var_name)
assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
cat("Outliers successfully removed", "n")
return(invisible(dt))
} else{
cat("Nothing changed", "n")
return(invisible(var_name)) }}
outlierKD(MOREX, sdo7)
## Outliers identified: 152 nPropotion (%) of outliers: 17.6 nMean of the outliers: 5 nMean without removing outliers: 1.87 nMean if we remove outliers: 1.31 nDo you want to remove outliers and to replace with NA? [yes/no]:
## Nothing changed nInquring into MEX1
OutlierS were prefered to be kept in the dataset.
outlierKD <- function(dt, var) {
var_name <- eval(substitute(var),eval(dt))
na1 <- sum(is.na(var_name))
m1 <- mean(var_name, na.rm = T)
par(mfrow=c(2, 2), oma=c(0,0,3,0))
boxplot(var_name, main="With outliers")
hist(var_name, main="With outliers", xlab=NA, ylab=NA)
outlier <- boxplot.stats(var_name)$out
mo <- mean(outlier)
var_name <- ifelse(var_name %in% outlier, NA, var_name)
boxplot(var_name, main="Without outliers")
hist(var_name, main="Without outliers", xlab=NA, ylab=NA)
title("Outlier Check", outer=TRUE)
na2 <- sum(is.na(var_name))
cat("Outliers identified:", na2 - na1, "n")
cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n")
cat("Mean of the outliers:", round(mo, 2), "n")
m2 <- mean(var_name, na.rm = T)
cat("Mean without removing outliers:", round(m1, 2), "n")
cat("Mean if we remove outliers:", round(m2, 2), "n")
response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ")
if(response == "y" | response == "yes"){
dt[as.character(substitute(var))] <- invisible(var_name)
assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv)
cat("Outliers successfully removed", "n")
return(invisible(dt))
} else{
cat("Nothing changed", "n")
return(invisible(var_name)) }}
outlierKD(MOREX, MEX1)
## Outliers identified: 40 nPropotion (%) of outliers: 4.1 nMean of the outliers: 7 nMean without removing outliers: 2.23 nMean if we remove outliers: 2.03 nDo you want to remove outliers and to replace with NA? [yes/no]:
## Nothing changed nTests of Normality
None of the varibles were found to meet normal distribution assumtion. Since multivariate normality assumption is violated , it was decided to emply MLR estimator (\(maximum likelihood with robust corrections to standard errors\)) in the structural equation models.
MOREX %>%
gather(variable, value) %>%
ggplot() +
aes(x = value) +
geom_freqpoly() +
facet_wrap(~variable)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
shapiro.test(MOREX$DISC)##
## Shapiro-Wilk normality test
##
## data: MOREX$DISC
## W = 0.96166, p-value = 1.122e-15skewness(MOREX$DISC)## [1] 0.4223075kurtosis(MOREX$DISC)## [1] 2.427429ggqqplot(MOREX$DISC)
hist(MOREX$DISC)
ggdensity(MOREX$DISC)
shapiro.test(MOREX$MEX)##
## Shapiro-Wilk normality test
##
## data: MOREX$MEX
## W = 0.97359, p-value = 1.246e-12skewness(MOREX$MEX)## [1] -0.1765252kurtosis(MOREX$MEX)## [1] 2.240145ggqqplot(MOREX$MEX)
hist(MOREX$MEX)
ggdensity(MOREX$MEX)
shapiro.test(MOREX$SDO)##
## Shapiro-Wilk normality test
##
## data: MOREX$SDO
## W = 0.96801, p-value = 3.761e-14skewness(MOREX$SDO)## [1] 0.3884446kurtosis(MOREX$SDO)## [1] 2.542652ggqqplot(MOREX$SDO)
hist(MOREX$SDO)
ggdensity(MOREX$SDO)
shapiro.test(MOREX$RWA)##
## Shapiro-Wilk normality test
##
## data: MOREX$RWA
## W = 0.95527, p-value < 2.2e-16skewness(MOREX$RWA)## [1] 0.448127kurtosis(MOREX$RWA)## [1] 2.414649ggqqplot(MOREX$RWA)
hist(MOREX$RWA)
ggdensity(MOREX$RWA)
shapiro.test(MOREX$MEX1)##
## Shapiro-Wilk normality test
##
## data: MOREX$MEX1
## W = 0.75259, p-value < 2.2e-16skewness(MOREX$MEX1)## [1] 1.323717kurtosis(MOREX$MEX1)## [1] 3.910982ggqqplot(MOREX$MEX1)
hist(MOREX$MEX1)
ggdensity(MOREX$MEX1)
shapiro.test(MOREX$MEX2)##
## Shapiro-Wilk normality test
##
## data: MOREX$MEX2
## W = 0.88968, p-value < 2.2e-16skewness(MOREX$MEX2)## [1] -0.3320415kurtosis(MOREX$MEX2)## [1] 1.887003ggqqplot(MOREX$MEX2)
hist(MOREX$MEX2)
ggdensity(MOREX$MEX2)
shapiro.test(MOREX$MEX3)##
## Shapiro-Wilk normality test
##
## data: MOREX$MEX3
## W = 0.81768, p-value < 2.2e-16skewness(MOREX$MEX3)## [1] -0.8230591kurtosis(MOREX$MEX3)## [1] 2.526161ggqqplot(MOREX$MEX3)
hist(MOREX$MEX3)
ggdensity(MOREX$MEX3)
shapiro.test(MOREX$NEG1)##
## Shapiro-Wilk normality test
##
## data: MOREX$NEG1
## W = 0.80136, p-value < 2.2e-16skewness(MOREX$NEG1)## [1] 0.8830489kurtosis(MOREX$NEG1)## [1] 2.486659ggqqplot(MOREX$NEG1)
hist(MOREX$NEG1)
ggdensity(MOREX$NEG1)
shapiro.test(MOREX$NEG2)##
## Shapiro-Wilk normality test
##
## data: MOREX$NEG2
## W = 0.84597, p-value < 2.2e-16skewness(MOREX$NEG2)## [1] 0.08784638kurtosis(MOREX$NEG2)## [1] 1.454401ggqqplot(MOREX$NEG2)
hist(MOREX$NEG2)
ggdensity(MOREX$NEG2)
shapiro.test(MOREX$NEG3)##
## Shapiro-Wilk normality test
##
## data: MOREX$NEG3
## W = 0.90961, p-value < 2.2e-16skewness(MOREX$NEG3)## [1] 0.1077017kurtosis(MOREX$NEG3)## [1] 1.830878ggqqplot(MOREX$NEG3)
hist(MOREX$NEG3)
ggdensity(MOREX$NEG3)
shapiro.test(MOREX$NEG5)##
## Shapiro-Wilk normality test
##
## data: MOREX$NEG5
## W = 0.83852, p-value < 2.2e-16skewness(MOREX$NEG5)## [1] 0.7738774kurtosis(MOREX$NEG5)## [1] 2.355297ggqqplot(MOREX$NEG5)
hist(MOREX$NEG5)
ggdensity(MOREX$NEG5)
shapiro.test(MOREX$NEGn)##
## Shapiro-Wilk normality test
##
## data: MOREX$NEGn
## W = 0.83747, p-value < 2.2e-16skewness(MOREX$NEGn)## [1] 0.8748391kurtosis(MOREX$NEGn)## [1] 2.803275ggqqplot(MOREX$NEGn)
hist(MOREX$NEGn)
ggdensity(MOREX$NEGn)
shapiro.test(MOREX$sdo1)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo1
## W = 0.83322, p-value < 2.2e-16skewness(MOREX$sdo1)## [1] 0.7127712kurtosis(MOREX$sdo1)## [1] 2.321097ggqqplot(MOREX$sdo1)
hist(MOREX$sdo1)
ggdensity(MOREX$sdo1)
shapiro.test(MOREX$sdo2)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo2
## W = 0.69787, p-value < 2.2e-16skewness(MOREX$sdo2)## [1] 1.321446kurtosis(MOREX$sdo2)## [1] 3.453847ggqqplot(MOREX$sdo2)
hist(MOREX$sdo2)
ggdensity(MOREX$sdo2)
shapiro.test(MOREX$sdo3)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo3
## W = 0.84402, p-value < 2.2e-16skewness(MOREX$sdo3)## [1] 0.5605391kurtosis(MOREX$sdo3)## [1] 1.969982ggqqplot(MOREX$sdo3)
hist(MOREX$sdo3)
ggdensity(MOREX$sdo3)
shapiro.test(MOREX$sdo4)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo4
## W = 0.7569, p-value < 2.2e-16skewness(MOREX$sdo4)## [1] 1.061738kurtosis(MOREX$sdo4)## [1] 2.7928ggqqplot(MOREX$sdo4)
hist(MOREX$sdo4)
ggdensity(MOREX$sdo4)
shapiro.test(MOREX$sdo5)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo5
## W = 0.86441, p-value < 2.2e-16skewness(MOREX$sdo5)## [1] 0.3695372kurtosis(MOREX$sdo5)## [1] 1.734078ggqqplot(MOREX$sdo5)
hist(MOREX$sdo5)
ggdensity(MOREX$sdo5)
shapiro.test(MOREX$sdo6)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo6
## W = 0.87322, p-value < 2.2e-16skewness(MOREX$sdo6)## [1] -0.2187444kurtosis(MOREX$sdo6)## [1] 1.643743ggqqplot(MOREX$sdo6)
hist(MOREX$sdo6)
ggdensity(MOREX$sdo6)
shapiro.test(MOREX$sdo7)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo7
## W = 0.63873, p-value < 2.2e-16skewness(MOREX$sdo7)## [1] 1.89676kurtosis(MOREX$sdo7)## [1] 5.896715ggqqplot(MOREX$sdo7)
hist(MOREX$sdo7)
ggdensity(MOREX$sdo7)
shapiro.test(MOREX$sdo8)##
## Shapiro-Wilk normality test
##
## data: MOREX$sdo8
## W = 0.79258, p-value < 2.2e-16skewness(MOREX$sdo8)## [1] 1.091213kurtosis(MOREX$sdo8)## [1] 3.172406ggqqplot(MOREX$sdo8)
hist(MOREX$sdo8)
ggdensity(MOREX$sdo8)
shapiro.test(MOREX$rwa1)##
## Shapiro-Wilk normality test
##
## data: MOREX$rwa1
## W = 0.85753, p-value < 2.2e-16skewness(MOREX$rwa1)## [1] 0.661101kurtosis(MOREX$rwa1)## [1] 2.348878ggqqplot(MOREX$rwa1)
hist(MOREX$rwa1)
ggdensity(MOREX$rwa1)
shapiro.test(MOREX$rwa2)##
## Shapiro-Wilk normality test
##
## data: MOREX$rwa2
## W = 0.84719, p-value < 2.2e-16skewness(MOREX$rwa2)## [1] 0.350081kurtosis(MOREX$rwa2)## [1] 1.626343ggqqplot(MOREX$rwa2)
hist(MOREX$rwa2)
ggdensity(MOREX$rwa2)
shapiro.test(MOREX$rwa3)##
## Shapiro-Wilk normality test
##
## data: MOREX$rwa3
## W = 0.84745, p-value < 2.2e-16skewness(MOREX$rwa3)## [1] 0.7333391kurtosis(MOREX$rwa3)## [1] 2.35392ggqqplot(MOREX$rwa3)
hist(MOREX$rwa3)
ggdensity(MOREX$rwa3)
shapiro.test(MOREX$rwa4)##
## Shapiro-Wilk normality test
##
## data: MOREX$rwa4
## W = 0.79787, p-value < 2.2e-16skewness(MOREX$rwa4)## [1] 0.9014101kurtosis(MOREX$rwa4)## [1] 2.695604ggqqplot(MOREX$rwa4)
hist(MOREX$rwa4)
ggdensity(MOREX$rwa4)
shapiro.test(MOREX$rwa5)##
## Shapiro-Wilk normality test
##
## data: MOREX$rwa5
## W = 0.80114, p-value < 2.2e-16skewness(MOREX$rwa5)## [1] 0.8683063kurtosis(MOREX$rwa5)## [1] 2.453487ggqqplot(MOREX$rwa5)
hist(MOREX$rwa5)
ggdensity(MOREX$rwa5)
shapiro.test(MOREX$rwa6)##
## Shapiro-Wilk normality test
##
## data: MOREX$rwa6
## W = 0.77901, p-value < 2.2e-16skewness(MOREX$rwa6)## [1] 0.9471383kurtosis(MOREX$rwa6)## [1] 2.636909ggqqplot(MOREX$rwa6)
hist(MOREX$rwa6)
ggdensity(MOREX$rwa6)
Some Multivariate Normality Tests
MOREX%>%
select(-gender, -age)%>%
mvn(mvnTest = "royston")## $multivariateNormality
## Test H p value MVN
## 1 Royston 2387.091 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk NEG1 0.8014 <0.001 NO
## 2 Shapiro-Wilk NEG2 0.8460 <0.001 NO
## 3 Shapiro-Wilk NEG3 0.9096 <0.001 NO
## 4 Shapiro-Wilk NEG5 0.8385 <0.001 NO
## 5 Shapiro-Wilk NEGn 0.8375 <0.001 NO
## 6 Shapiro-Wilk DISC 0.9617 <0.001 NO
## 7 Shapiro-Wilk MIN 0.9736 <0.001 NO
## 8 Shapiro-Wilk MEX 0.9736 <0.001 NO
## 9 Shapiro-Wilk SDO 0.9680 <0.001 NO
## 10 Shapiro-Wilk RWA 0.9553 <0.001 NO
## 11 Shapiro-Wilk sdo1 0.8332 <0.001 NO
## 12 Shapiro-Wilk sdo2 0.6979 <0.001 NO
## 13 Shapiro-Wilk sdo3 0.8440 <0.001 NO
## 14 Shapiro-Wilk sdo4 0.7569 <0.001 NO
## 15 Shapiro-Wilk sdo5 0.8644 <0.001 NO
## 16 Shapiro-Wilk sdo6 0.8732 <0.001 NO
## 17 Shapiro-Wilk sdo7 0.6387 <0.001 NO
## 18 Shapiro-Wilk sdo8 0.7926 <0.001 NO
## 19 Shapiro-Wilk MEX1 0.7526 <0.001 NO
## 20 Shapiro-Wilk MEX2 0.8897 <0.001 NO
## 21 Shapiro-Wilk MEX3 0.8177 <0.001 NO
## 22 Shapiro-Wilk rwa1 0.8575 <0.001 NO
## 23 Shapiro-Wilk rwa2 0.8472 <0.001 NO
## 24 Shapiro-Wilk rwa3 0.8475 <0.001 NO
## 25 Shapiro-Wilk rwa4 0.7979 <0.001 NO
## 26 Shapiro-Wilk rwa5 0.8011 <0.001 NO
## 27 Shapiro-Wilk rwa6 0.7790 <0.001 NO
## 28 Shapiro-Wilk SDOI 0.8551 <0.001 NO
## 29 Shapiro-Wilk SDOII 0.8765 <0.001 NO
## 30 Shapiro-Wilk SDOIII 0.9368 <0.001 NO
## 31 Shapiro-Wilk SDOIV 0.7833 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th
## NEG1 1015 2.730049 2.018170 2.000000 1 7.000000 1.000000 4.000000
## NEG2 1015 3.827586 2.369020 4.000000 1 7.000000 1.000000 6.000000
## NEG3 1015 3.866010 2.039979 4.000000 1 7.000000 2.000000 6.000000
## NEG5 1015 2.903448 2.000626 2.000000 1 7.000000 1.000000 4.000000
## NEGn 1015 2.725123 1.829063 2.000000 1 7.000000 1.000000 4.000000
## DISC 1015 3.210443 1.527786 3.000000 1 7.000000 2.000000 4.200000
## MIN 1015 3.931363 1.556165 4.000000 1 7.000000 3.000000 5.000000
## MEX 1015 4.068637 1.556165 4.000000 1 7.000000 3.000000 5.000000
## SDO 1015 2.821552 1.230008 2.750000 1 7.000000 1.750000 3.750000
## RWA 1015 2.799015 1.316253 2.666667 1 6.833333 1.666667 3.666667
## sdo1 1015 2.760591 1.899035 2.000000 1 7.000000 1.000000 4.000000
## sdo2 1015 2.243350 1.873434 1.000000 1 7.000000 1.000000 3.000000
## sdo3 1015 3.062069 2.090433 3.000000 1 7.000000 1.000000 5.000000
## sdo4 1015 2.527094 1.996794 1.000000 1 7.000000 1.000000 4.000000
## sdo5 1015 3.369458 2.163543 3.000000 1 7.000000 1.000000 5.000000
## sdo6 1015 4.307389 2.221931 4.000000 1 7.000000 2.000000 7.000000
## sdo7 1015 1.865025 1.501646 1.000000 1 7.000000 1.000000 2.000000
## sdo8 1015 2.437438 1.763586 2.000000 1 7.000000 1.000000 4.000000
## MEX1 1015 2.228571 1.662017 1.000000 1 7.000000 1.000000 3.000000
## MEX2 1015 4.655172 2.012611 5.000000 1 7.000000 3.000000 7.000000
## MEX3 1015 5.322167 1.894571 6.000000 1 7.000000 4.000000 7.000000
## rwa1 1015 2.814778 1.823358 2.000000 1 7.000000 1.000000 4.000000
## rwa2 1015 3.448276 2.273472 3.000000 1 7.000000 1.000000 6.000000
## rwa3 1015 2.808867 1.883417 2.000000 1 7.000000 1.000000 4.000000
## rwa4 1015 2.555665 1.842573 2.000000 1 7.000000 1.000000 4.000000
## rwa5 1015 2.659113 1.956644 2.000000 1 7.000000 1.000000 4.000000
## rwa6 1015 2.507389 1.882048 1.000000 1 7.000000 1.000000 4.000000
## SDOI 1015 2.501970 1.603742 2.000000 1 7.000000 1.000000 3.500000
## SDOII 1015 2.794581 1.761807 2.500000 1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000 1 7.000000 2.500000 5.500000
## SDOIV 1015 2.151232 1.476989 1.500000 1 7.000000 1.000000 3.000000
## Skew Kurtosis
## NEG1 0.88174425 -0.51823794
## NEG2 0.08771659 -1.54846305
## NEG3 0.10754259 -1.17272762
## NEG5 0.77273398 -0.64934212
## NEGn 0.87354659 -0.20224616
## DISC 0.42168352 -0.57735149
## MIN 0.17626442 -0.76426732
## MEX -0.17626442 -0.76426732
## SDO 0.38787069 -0.46235602
## RWA 0.44746486 -0.59010696
## sdo1 0.71171812 -0.68347406
## sdo2 1.31949395 0.44704465
## sdo3 0.55971096 -1.03389821
## sdo4 1.06016930 -0.21270003
## sdo5 0.36899118 -1.26933685
## sdo6 -0.21842121 -1.35949408
## sdo7 1.89395727 2.88510149
## sdo8 1.08960095 0.16615795
## MEX1 1.32176151 0.90327954
## MEX2 -0.33155094 -1.11671341
## MEX3 -0.82184307 -0.47881378
## rwa1 0.66012423 -0.65574790
## rwa2 0.34956373 -1.37686027
## rwa3 0.73225563 -0.65071591
## rwa4 0.90007829 -0.30970443
## rwa5 0.86702345 -0.55134503
## rwa6 0.94573891 -0.36828477
## SDOI 0.93727862 0.07714219
## SDOII 0.68754350 -0.51024139
## SDOIII 0.01756050 -1.04435488
## SDOIV 1.38799904 1.37152716MOREX%>%
select(-gender, -age)%>%
mvn(mvnTest = "hz")## $multivariateNormality
## Test HZ p value MVN
## 1 Henze-Zirkler 4060 0 NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk NEG1 0.8014 <0.001 NO
## 2 Shapiro-Wilk NEG2 0.8460 <0.001 NO
## 3 Shapiro-Wilk NEG3 0.9096 <0.001 NO
## 4 Shapiro-Wilk NEG5 0.8385 <0.001 NO
## 5 Shapiro-Wilk NEGn 0.8375 <0.001 NO
## 6 Shapiro-Wilk DISC 0.9617 <0.001 NO
## 7 Shapiro-Wilk MIN 0.9736 <0.001 NO
## 8 Shapiro-Wilk MEX 0.9736 <0.001 NO
## 9 Shapiro-Wilk SDO 0.9680 <0.001 NO
## 10 Shapiro-Wilk RWA 0.9553 <0.001 NO
## 11 Shapiro-Wilk sdo1 0.8332 <0.001 NO
## 12 Shapiro-Wilk sdo2 0.6979 <0.001 NO
## 13 Shapiro-Wilk sdo3 0.8440 <0.001 NO
## 14 Shapiro-Wilk sdo4 0.7569 <0.001 NO
## 15 Shapiro-Wilk sdo5 0.8644 <0.001 NO
## 16 Shapiro-Wilk sdo6 0.8732 <0.001 NO
## 17 Shapiro-Wilk sdo7 0.6387 <0.001 NO
## 18 Shapiro-Wilk sdo8 0.7926 <0.001 NO
## 19 Shapiro-Wilk MEX1 0.7526 <0.001 NO
## 20 Shapiro-Wilk MEX2 0.8897 <0.001 NO
## 21 Shapiro-Wilk MEX3 0.8177 <0.001 NO
## 22 Shapiro-Wilk rwa1 0.8575 <0.001 NO
## 23 Shapiro-Wilk rwa2 0.8472 <0.001 NO
## 24 Shapiro-Wilk rwa3 0.8475 <0.001 NO
## 25 Shapiro-Wilk rwa4 0.7979 <0.001 NO
## 26 Shapiro-Wilk rwa5 0.8011 <0.001 NO
## 27 Shapiro-Wilk rwa6 0.7790 <0.001 NO
## 28 Shapiro-Wilk SDOI 0.8551 <0.001 NO
## 29 Shapiro-Wilk SDOII 0.8765 <0.001 NO
## 30 Shapiro-Wilk SDOIII 0.9368 <0.001 NO
## 31 Shapiro-Wilk SDOIV 0.7833 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th
## NEG1 1015 2.730049 2.018170 2.000000 1 7.000000 1.000000 4.000000
## NEG2 1015 3.827586 2.369020 4.000000 1 7.000000 1.000000 6.000000
## NEG3 1015 3.866010 2.039979 4.000000 1 7.000000 2.000000 6.000000
## NEG5 1015 2.903448 2.000626 2.000000 1 7.000000 1.000000 4.000000
## NEGn 1015 2.725123 1.829063 2.000000 1 7.000000 1.000000 4.000000
## DISC 1015 3.210443 1.527786 3.000000 1 7.000000 2.000000 4.200000
## MIN 1015 3.931363 1.556165 4.000000 1 7.000000 3.000000 5.000000
## MEX 1015 4.068637 1.556165 4.000000 1 7.000000 3.000000 5.000000
## SDO 1015 2.821552 1.230008 2.750000 1 7.000000 1.750000 3.750000
## RWA 1015 2.799015 1.316253 2.666667 1 6.833333 1.666667 3.666667
## sdo1 1015 2.760591 1.899035 2.000000 1 7.000000 1.000000 4.000000
## sdo2 1015 2.243350 1.873434 1.000000 1 7.000000 1.000000 3.000000
## sdo3 1015 3.062069 2.090433 3.000000 1 7.000000 1.000000 5.000000
## sdo4 1015 2.527094 1.996794 1.000000 1 7.000000 1.000000 4.000000
## sdo5 1015 3.369458 2.163543 3.000000 1 7.000000 1.000000 5.000000
## sdo6 1015 4.307389 2.221931 4.000000 1 7.000000 2.000000 7.000000
## sdo7 1015 1.865025 1.501646 1.000000 1 7.000000 1.000000 2.000000
## sdo8 1015 2.437438 1.763586 2.000000 1 7.000000 1.000000 4.000000
## MEX1 1015 2.228571 1.662017 1.000000 1 7.000000 1.000000 3.000000
## MEX2 1015 4.655172 2.012611 5.000000 1 7.000000 3.000000 7.000000
## MEX3 1015 5.322167 1.894571 6.000000 1 7.000000 4.000000 7.000000
## rwa1 1015 2.814778 1.823358 2.000000 1 7.000000 1.000000 4.000000
## rwa2 1015 3.448276 2.273472 3.000000 1 7.000000 1.000000 6.000000
## rwa3 1015 2.808867 1.883417 2.000000 1 7.000000 1.000000 4.000000
## rwa4 1015 2.555665 1.842573 2.000000 1 7.000000 1.000000 4.000000
## rwa5 1015 2.659113 1.956644 2.000000 1 7.000000 1.000000 4.000000
## rwa6 1015 2.507389 1.882048 1.000000 1 7.000000 1.000000 4.000000
## SDOI 1015 2.501970 1.603742 2.000000 1 7.000000 1.000000 3.500000
## SDOII 1015 2.794581 1.761807 2.500000 1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000 1 7.000000 2.500000 5.500000
## SDOIV 1015 2.151232 1.476989 1.500000 1 7.000000 1.000000 3.000000
## Skew Kurtosis
## NEG1 0.88174425 -0.51823794
## NEG2 0.08771659 -1.54846305
## NEG3 0.10754259 -1.17272762
## NEG5 0.77273398 -0.64934212
## NEGn 0.87354659 -0.20224616
## DISC 0.42168352 -0.57735149
## MIN 0.17626442 -0.76426732
## MEX -0.17626442 -0.76426732
## SDO 0.38787069 -0.46235602
## RWA 0.44746486 -0.59010696
## sdo1 0.71171812 -0.68347406
## sdo2 1.31949395 0.44704465
## sdo3 0.55971096 -1.03389821
## sdo4 1.06016930 -0.21270003
## sdo5 0.36899118 -1.26933685
## sdo6 -0.21842121 -1.35949408
## sdo7 1.89395727 2.88510149
## sdo8 1.08960095 0.16615795
## MEX1 1.32176151 0.90327954
## MEX2 -0.33155094 -1.11671341
## MEX3 -0.82184307 -0.47881378
## rwa1 0.66012423 -0.65574790
## rwa2 0.34956373 -1.37686027
## rwa3 0.73225563 -0.65071591
## rwa4 0.90007829 -0.30970443
## rwa5 0.86702345 -0.55134503
## rwa6 0.94573891 -0.36828477
## SDOI 0.93727862 0.07714219
## SDOII 0.68754350 -0.51024139
## SDOIII 0.01756050 -1.04435488
## SDOIV 1.38799904 1.37152716MOREX%>%
select(-gender, -age)%>%
mvn(mvnTest = "energy")## Warning in sqrt(lambda): NaNs produced## $multivariateNormality
## Test Statistic p value MVN
## 1 E-statistic NA NA NA
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk NEG1 0.8014 <0.001 NO
## 2 Shapiro-Wilk NEG2 0.8460 <0.001 NO
## 3 Shapiro-Wilk NEG3 0.9096 <0.001 NO
## 4 Shapiro-Wilk NEG5 0.8385 <0.001 NO
## 5 Shapiro-Wilk NEGn 0.8375 <0.001 NO
## 6 Shapiro-Wilk DISC 0.9617 <0.001 NO
## 7 Shapiro-Wilk MIN 0.9736 <0.001 NO
## 8 Shapiro-Wilk MEX 0.9736 <0.001 NO
## 9 Shapiro-Wilk SDO 0.9680 <0.001 NO
## 10 Shapiro-Wilk RWA 0.9553 <0.001 NO
## 11 Shapiro-Wilk sdo1 0.8332 <0.001 NO
## 12 Shapiro-Wilk sdo2 0.6979 <0.001 NO
## 13 Shapiro-Wilk sdo3 0.8440 <0.001 NO
## 14 Shapiro-Wilk sdo4 0.7569 <0.001 NO
## 15 Shapiro-Wilk sdo5 0.8644 <0.001 NO
## 16 Shapiro-Wilk sdo6 0.8732 <0.001 NO
## 17 Shapiro-Wilk sdo7 0.6387 <0.001 NO
## 18 Shapiro-Wilk sdo8 0.7926 <0.001 NO
## 19 Shapiro-Wilk MEX1 0.7526 <0.001 NO
## 20 Shapiro-Wilk MEX2 0.8897 <0.001 NO
## 21 Shapiro-Wilk MEX3 0.8177 <0.001 NO
## 22 Shapiro-Wilk rwa1 0.8575 <0.001 NO
## 23 Shapiro-Wilk rwa2 0.8472 <0.001 NO
## 24 Shapiro-Wilk rwa3 0.8475 <0.001 NO
## 25 Shapiro-Wilk rwa4 0.7979 <0.001 NO
## 26 Shapiro-Wilk rwa5 0.8011 <0.001 NO
## 27 Shapiro-Wilk rwa6 0.7790 <0.001 NO
## 28 Shapiro-Wilk SDOI 0.8551 <0.001 NO
## 29 Shapiro-Wilk SDOII 0.8765 <0.001 NO
## 30 Shapiro-Wilk SDOIII 0.9368 <0.001 NO
## 31 Shapiro-Wilk SDOIV 0.7833 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th
## NEG1 1015 2.730049 2.018170 2.000000 1 7.000000 1.000000 4.000000
## NEG2 1015 3.827586 2.369020 4.000000 1 7.000000 1.000000 6.000000
## NEG3 1015 3.866010 2.039979 4.000000 1 7.000000 2.000000 6.000000
## NEG5 1015 2.903448 2.000626 2.000000 1 7.000000 1.000000 4.000000
## NEGn 1015 2.725123 1.829063 2.000000 1 7.000000 1.000000 4.000000
## DISC 1015 3.210443 1.527786 3.000000 1 7.000000 2.000000 4.200000
## MIN 1015 3.931363 1.556165 4.000000 1 7.000000 3.000000 5.000000
## MEX 1015 4.068637 1.556165 4.000000 1 7.000000 3.000000 5.000000
## SDO 1015 2.821552 1.230008 2.750000 1 7.000000 1.750000 3.750000
## RWA 1015 2.799015 1.316253 2.666667 1 6.833333 1.666667 3.666667
## sdo1 1015 2.760591 1.899035 2.000000 1 7.000000 1.000000 4.000000
## sdo2 1015 2.243350 1.873434 1.000000 1 7.000000 1.000000 3.000000
## sdo3 1015 3.062069 2.090433 3.000000 1 7.000000 1.000000 5.000000
## sdo4 1015 2.527094 1.996794 1.000000 1 7.000000 1.000000 4.000000
## sdo5 1015 3.369458 2.163543 3.000000 1 7.000000 1.000000 5.000000
## sdo6 1015 4.307389 2.221931 4.000000 1 7.000000 2.000000 7.000000
## sdo7 1015 1.865025 1.501646 1.000000 1 7.000000 1.000000 2.000000
## sdo8 1015 2.437438 1.763586 2.000000 1 7.000000 1.000000 4.000000
## MEX1 1015 2.228571 1.662017 1.000000 1 7.000000 1.000000 3.000000
## MEX2 1015 4.655172 2.012611 5.000000 1 7.000000 3.000000 7.000000
## MEX3 1015 5.322167 1.894571 6.000000 1 7.000000 4.000000 7.000000
## rwa1 1015 2.814778 1.823358 2.000000 1 7.000000 1.000000 4.000000
## rwa2 1015 3.448276 2.273472 3.000000 1 7.000000 1.000000 6.000000
## rwa3 1015 2.808867 1.883417 2.000000 1 7.000000 1.000000 4.000000
## rwa4 1015 2.555665 1.842573 2.000000 1 7.000000 1.000000 4.000000
## rwa5 1015 2.659113 1.956644 2.000000 1 7.000000 1.000000 4.000000
## rwa6 1015 2.507389 1.882048 1.000000 1 7.000000 1.000000 4.000000
## SDOI 1015 2.501970 1.603742 2.000000 1 7.000000 1.000000 3.500000
## SDOII 1015 2.794581 1.761807 2.500000 1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000 1 7.000000 2.500000 5.500000
## SDOIV 1015 2.151232 1.476989 1.500000 1 7.000000 1.000000 3.000000
## Skew Kurtosis
## NEG1 0.88174425 -0.51823794
## NEG2 0.08771659 -1.54846305
## NEG3 0.10754259 -1.17272762
## NEG5 0.77273398 -0.64934212
## NEGn 0.87354659 -0.20224616
## DISC 0.42168352 -0.57735149
## MIN 0.17626442 -0.76426732
## MEX -0.17626442 -0.76426732
## SDO 0.38787069 -0.46235602
## RWA 0.44746486 -0.59010696
## sdo1 0.71171812 -0.68347406
## sdo2 1.31949395 0.44704465
## sdo3 0.55971096 -1.03389821
## sdo4 1.06016930 -0.21270003
## sdo5 0.36899118 -1.26933685
## sdo6 -0.21842121 -1.35949408
## sdo7 1.89395727 2.88510149
## sdo8 1.08960095 0.16615795
## MEX1 1.32176151 0.90327954
## MEX2 -0.33155094 -1.11671341
## MEX3 -0.82184307 -0.47881378
## rwa1 0.66012423 -0.65574790
## rwa2 0.34956373 -1.37686027
## rwa3 0.73225563 -0.65071591
## rwa4 0.90007829 -0.30970443
## rwa5 0.86702345 -0.55134503
## rwa6 0.94573891 -0.36828477
## SDOI 0.93727862 0.07714219
## SDOII 0.68754350 -0.51024139
## SDOIII 0.01756050 -1.04435488
## SDOIV 1.38799904 1.37152716MOREX%>%
select(-gender, -age)%>%
mvn(mvnTest = "dh")## $multivariateNormality
## Test E df p value MVN
## 1 Doornik-Hansen NaN 62 NaN NA
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk NEG1 0.8014 <0.001 NO
## 2 Shapiro-Wilk NEG2 0.8460 <0.001 NO
## 3 Shapiro-Wilk NEG3 0.9096 <0.001 NO
## 4 Shapiro-Wilk NEG5 0.8385 <0.001 NO
## 5 Shapiro-Wilk NEGn 0.8375 <0.001 NO
## 6 Shapiro-Wilk DISC 0.9617 <0.001 NO
## 7 Shapiro-Wilk MIN 0.9736 <0.001 NO
## 8 Shapiro-Wilk MEX 0.9736 <0.001 NO
## 9 Shapiro-Wilk SDO 0.9680 <0.001 NO
## 10 Shapiro-Wilk RWA 0.9553 <0.001 NO
## 11 Shapiro-Wilk sdo1 0.8332 <0.001 NO
## 12 Shapiro-Wilk sdo2 0.6979 <0.001 NO
## 13 Shapiro-Wilk sdo3 0.8440 <0.001 NO
## 14 Shapiro-Wilk sdo4 0.7569 <0.001 NO
## 15 Shapiro-Wilk sdo5 0.8644 <0.001 NO
## 16 Shapiro-Wilk sdo6 0.8732 <0.001 NO
## 17 Shapiro-Wilk sdo7 0.6387 <0.001 NO
## 18 Shapiro-Wilk sdo8 0.7926 <0.001 NO
## 19 Shapiro-Wilk MEX1 0.7526 <0.001 NO
## 20 Shapiro-Wilk MEX2 0.8897 <0.001 NO
## 21 Shapiro-Wilk MEX3 0.8177 <0.001 NO
## 22 Shapiro-Wilk rwa1 0.8575 <0.001 NO
## 23 Shapiro-Wilk rwa2 0.8472 <0.001 NO
## 24 Shapiro-Wilk rwa3 0.8475 <0.001 NO
## 25 Shapiro-Wilk rwa4 0.7979 <0.001 NO
## 26 Shapiro-Wilk rwa5 0.8011 <0.001 NO
## 27 Shapiro-Wilk rwa6 0.7790 <0.001 NO
## 28 Shapiro-Wilk SDOI 0.8551 <0.001 NO
## 29 Shapiro-Wilk SDOII 0.8765 <0.001 NO
## 30 Shapiro-Wilk SDOIII 0.9368 <0.001 NO
## 31 Shapiro-Wilk SDOIV 0.7833 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th
## NEG1 1015 2.730049 2.018170 2.000000 1 7.000000 1.000000 4.000000
## NEG2 1015 3.827586 2.369020 4.000000 1 7.000000 1.000000 6.000000
## NEG3 1015 3.866010 2.039979 4.000000 1 7.000000 2.000000 6.000000
## NEG5 1015 2.903448 2.000626 2.000000 1 7.000000 1.000000 4.000000
## NEGn 1015 2.725123 1.829063 2.000000 1 7.000000 1.000000 4.000000
## DISC 1015 3.210443 1.527786 3.000000 1 7.000000 2.000000 4.200000
## MIN 1015 3.931363 1.556165 4.000000 1 7.000000 3.000000 5.000000
## MEX 1015 4.068637 1.556165 4.000000 1 7.000000 3.000000 5.000000
## SDO 1015 2.821552 1.230008 2.750000 1 7.000000 1.750000 3.750000
## RWA 1015 2.799015 1.316253 2.666667 1 6.833333 1.666667 3.666667
## sdo1 1015 2.760591 1.899035 2.000000 1 7.000000 1.000000 4.000000
## sdo2 1015 2.243350 1.873434 1.000000 1 7.000000 1.000000 3.000000
## sdo3 1015 3.062069 2.090433 3.000000 1 7.000000 1.000000 5.000000
## sdo4 1015 2.527094 1.996794 1.000000 1 7.000000 1.000000 4.000000
## sdo5 1015 3.369458 2.163543 3.000000 1 7.000000 1.000000 5.000000
## sdo6 1015 4.307389 2.221931 4.000000 1 7.000000 2.000000 7.000000
## sdo7 1015 1.865025 1.501646 1.000000 1 7.000000 1.000000 2.000000
## sdo8 1015 2.437438 1.763586 2.000000 1 7.000000 1.000000 4.000000
## MEX1 1015 2.228571 1.662017 1.000000 1 7.000000 1.000000 3.000000
## MEX2 1015 4.655172 2.012611 5.000000 1 7.000000 3.000000 7.000000
## MEX3 1015 5.322167 1.894571 6.000000 1 7.000000 4.000000 7.000000
## rwa1 1015 2.814778 1.823358 2.000000 1 7.000000 1.000000 4.000000
## rwa2 1015 3.448276 2.273472 3.000000 1 7.000000 1.000000 6.000000
## rwa3 1015 2.808867 1.883417 2.000000 1 7.000000 1.000000 4.000000
## rwa4 1015 2.555665 1.842573 2.000000 1 7.000000 1.000000 4.000000
## rwa5 1015 2.659113 1.956644 2.000000 1 7.000000 1.000000 4.000000
## rwa6 1015 2.507389 1.882048 1.000000 1 7.000000 1.000000 4.000000
## SDOI 1015 2.501970 1.603742 2.000000 1 7.000000 1.000000 3.500000
## SDOII 1015 2.794581 1.761807 2.500000 1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000 1 7.000000 2.500000 5.500000
## SDOIV 1015 2.151232 1.476989 1.500000 1 7.000000 1.000000 3.000000
## Skew Kurtosis
## NEG1 0.88174425 -0.51823794
## NEG2 0.08771659 -1.54846305
## NEG3 0.10754259 -1.17272762
## NEG5 0.77273398 -0.64934212
## NEGn 0.87354659 -0.20224616
## DISC 0.42168352 -0.57735149
## MIN 0.17626442 -0.76426732
## MEX -0.17626442 -0.76426732
## SDO 0.38787069 -0.46235602
## RWA 0.44746486 -0.59010696
## sdo1 0.71171812 -0.68347406
## sdo2 1.31949395 0.44704465
## sdo3 0.55971096 -1.03389821
## sdo4 1.06016930 -0.21270003
## sdo5 0.36899118 -1.26933685
## sdo6 -0.21842121 -1.35949408
## sdo7 1.89395727 2.88510149
## sdo8 1.08960095 0.16615795
## MEX1 1.32176151 0.90327954
## MEX2 -0.33155094 -1.11671341
## MEX3 -0.82184307 -0.47881378
## rwa1 0.66012423 -0.65574790
## rwa2 0.34956373 -1.37686027
## rwa3 0.73225563 -0.65071591
## rwa4 0.90007829 -0.30970443
## rwa5 0.86702345 -0.55134503
## rwa6 0.94573891 -0.36828477
## SDOI 0.93727862 0.07714219
## SDOII 0.68754350 -0.51024139
## SDOIII 0.01756050 -1.04435488
## SDOIV 1.38799904 1.37152716MOREX%>%
select(-gender, -age)%>%
mvn(mvnTest = "mardia")## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness -1778.53345490144 1 YES
## 2 Mardia Kurtosis -142.28285335133 0 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk NEG1 0.8014 <0.001 NO
## 2 Shapiro-Wilk NEG2 0.8460 <0.001 NO
## 3 Shapiro-Wilk NEG3 0.9096 <0.001 NO
## 4 Shapiro-Wilk NEG5 0.8385 <0.001 NO
## 5 Shapiro-Wilk NEGn 0.8375 <0.001 NO
## 6 Shapiro-Wilk DISC 0.9617 <0.001 NO
## 7 Shapiro-Wilk MIN 0.9736 <0.001 NO
## 8 Shapiro-Wilk MEX 0.9736 <0.001 NO
## 9 Shapiro-Wilk SDO 0.9680 <0.001 NO
## 10 Shapiro-Wilk RWA 0.9553 <0.001 NO
## 11 Shapiro-Wilk sdo1 0.8332 <0.001 NO
## 12 Shapiro-Wilk sdo2 0.6979 <0.001 NO
## 13 Shapiro-Wilk sdo3 0.8440 <0.001 NO
## 14 Shapiro-Wilk sdo4 0.7569 <0.001 NO
## 15 Shapiro-Wilk sdo5 0.8644 <0.001 NO
## 16 Shapiro-Wilk sdo6 0.8732 <0.001 NO
## 17 Shapiro-Wilk sdo7 0.6387 <0.001 NO
## 18 Shapiro-Wilk sdo8 0.7926 <0.001 NO
## 19 Shapiro-Wilk MEX1 0.7526 <0.001 NO
## 20 Shapiro-Wilk MEX2 0.8897 <0.001 NO
## 21 Shapiro-Wilk MEX3 0.8177 <0.001 NO
## 22 Shapiro-Wilk rwa1 0.8575 <0.001 NO
## 23 Shapiro-Wilk rwa2 0.8472 <0.001 NO
## 24 Shapiro-Wilk rwa3 0.8475 <0.001 NO
## 25 Shapiro-Wilk rwa4 0.7979 <0.001 NO
## 26 Shapiro-Wilk rwa5 0.8011 <0.001 NO
## 27 Shapiro-Wilk rwa6 0.7790 <0.001 NO
## 28 Shapiro-Wilk SDOI 0.8551 <0.001 NO
## 29 Shapiro-Wilk SDOII 0.8765 <0.001 NO
## 30 Shapiro-Wilk SDOIII 0.9368 <0.001 NO
## 31 Shapiro-Wilk SDOIV 0.7833 <0.001 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th
## NEG1 1015 2.730049 2.018170 2.000000 1 7.000000 1.000000 4.000000
## NEG2 1015 3.827586 2.369020 4.000000 1 7.000000 1.000000 6.000000
## NEG3 1015 3.866010 2.039979 4.000000 1 7.000000 2.000000 6.000000
## NEG5 1015 2.903448 2.000626 2.000000 1 7.000000 1.000000 4.000000
## NEGn 1015 2.725123 1.829063 2.000000 1 7.000000 1.000000 4.000000
## DISC 1015 3.210443 1.527786 3.000000 1 7.000000 2.000000 4.200000
## MIN 1015 3.931363 1.556165 4.000000 1 7.000000 3.000000 5.000000
## MEX 1015 4.068637 1.556165 4.000000 1 7.000000 3.000000 5.000000
## SDO 1015 2.821552 1.230008 2.750000 1 7.000000 1.750000 3.750000
## RWA 1015 2.799015 1.316253 2.666667 1 6.833333 1.666667 3.666667
## sdo1 1015 2.760591 1.899035 2.000000 1 7.000000 1.000000 4.000000
## sdo2 1015 2.243350 1.873434 1.000000 1 7.000000 1.000000 3.000000
## sdo3 1015 3.062069 2.090433 3.000000 1 7.000000 1.000000 5.000000
## sdo4 1015 2.527094 1.996794 1.000000 1 7.000000 1.000000 4.000000
## sdo5 1015 3.369458 2.163543 3.000000 1 7.000000 1.000000 5.000000
## sdo6 1015 4.307389 2.221931 4.000000 1 7.000000 2.000000 7.000000
## sdo7 1015 1.865025 1.501646 1.000000 1 7.000000 1.000000 2.000000
## sdo8 1015 2.437438 1.763586 2.000000 1 7.000000 1.000000 4.000000
## MEX1 1015 2.228571 1.662017 1.000000 1 7.000000 1.000000 3.000000
## MEX2 1015 4.655172 2.012611 5.000000 1 7.000000 3.000000 7.000000
## MEX3 1015 5.322167 1.894571 6.000000 1 7.000000 4.000000 7.000000
## rwa1 1015 2.814778 1.823358 2.000000 1 7.000000 1.000000 4.000000
## rwa2 1015 3.448276 2.273472 3.000000 1 7.000000 1.000000 6.000000
## rwa3 1015 2.808867 1.883417 2.000000 1 7.000000 1.000000 4.000000
## rwa4 1015 2.555665 1.842573 2.000000 1 7.000000 1.000000 4.000000
## rwa5 1015 2.659113 1.956644 2.000000 1 7.000000 1.000000 4.000000
## rwa6 1015 2.507389 1.882048 1.000000 1 7.000000 1.000000 4.000000
## SDOI 1015 2.501970 1.603742 2.000000 1 7.000000 1.000000 3.500000
## SDOII 1015 2.794581 1.761807 2.500000 1 7.000000 1.000000 4.000000
## SDOIII 1015 3.838424 1.900509 4.000000 1 7.000000 2.500000 5.500000
## SDOIV 1015 2.151232 1.476989 1.500000 1 7.000000 1.000000 3.000000
## Skew Kurtosis
## NEG1 0.88174425 -0.51823794
## NEG2 0.08771659 -1.54846305
## NEG3 0.10754259 -1.17272762
## NEG5 0.77273398 -0.64934212
## NEGn 0.87354659 -0.20224616
## DISC 0.42168352 -0.57735149
## MIN 0.17626442 -0.76426732
## MEX -0.17626442 -0.76426732
## SDO 0.38787069 -0.46235602
## RWA 0.44746486 -0.59010696
## sdo1 0.71171812 -0.68347406
## sdo2 1.31949395 0.44704465
## sdo3 0.55971096 -1.03389821
## sdo4 1.06016930 -0.21270003
## sdo5 0.36899118 -1.26933685
## sdo6 -0.21842121 -1.35949408
## sdo7 1.89395727 2.88510149
## sdo8 1.08960095 0.16615795
## MEX1 1.32176151 0.90327954
## MEX2 -0.33155094 -1.11671341
## MEX3 -0.82184307 -0.47881378
## rwa1 0.66012423 -0.65574790
## rwa2 0.34956373 -1.37686027
## rwa3 0.73225563 -0.65071591
## rwa4 0.90007829 -0.30970443
## rwa5 0.86702345 -0.55134503
## rwa6 0.94573891 -0.36828477
## SDOI 0.93727862 0.07714219
## SDOII 0.68754350 -0.51024139
## SDOIII 0.01756050 -1.04435488
## SDOIV 1.38799904 1.37152716Correlation Analyses
Correlation Between Sets of Items Representing the Dependent variable; discriminatory attitudes against the Roma.
MOREX%>%
select(NEG1, NEG2, NEG3, NEG5, NEGn)%>%
lowerCor()%>%
corPlot()## NEG1 NEG2 NEG3 NEG5 NEGn
## NEG1 1.00
## NEG2 0.57 1.00
## NEG3 0.34 0.32 1.00
## NEG5 0.46 0.43 0.46 1.00
## NEGn 0.44 0.42 0.43 0.59 1.00
cor.test(MOREX$NEG1 , MOREX$NEG2)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG1 and MOREX$NEG2
## t = 21.872, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5230627 0.6067544
## sample estimates:
## cor
## 0.5663669ggplot(MOREX) +
aes(x = NEG1 , y = NEG2) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG1 , MOREX$NEG3)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG1 and MOREX$NEG3
## t = 11.323, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2794153 0.3887016
## sample estimates:
## cor
## 0.3351854ggplot(MOREX) +
aes(x = NEG1 , y = NEG3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG1 , MOREX$NEG5)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG1 and MOREX$NEG5
## t = 16.303, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4057574 0.5033208
## sample estimates:
## cor
## 0.4559076ggplot(MOREX) +
aes(x = NEG1 , y = NEG5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG1 , MOREX$NEGn)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG1 and MOREX$NEGn
## t = 15.463, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3858352 0.4854723
## sample estimates:
## cor
## 0.4369933ggplot(MOREX) +
aes(x = NEG1 , y = NEGn) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG2 , MOREX$NEG3)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG2 and MOREX$NEG3
## t = 10.929, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2686143 0.3787433
## sample estimates:
## cor
## 0.3247793ggplot(MOREX) +
aes(x = NEG2 , y = NEG3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor(MOREX$NEG2 , MOREX$NEG5)## [1] 0.427415ggplot(MOREX) +
aes(x = NEG2 , y = NEG5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG2 , MOREX$NEGn)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG2 and MOREX$NEGn
## t = 14.715, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3676235 0.4690833
## sample estimates:
## cor
## 0.4196634ggplot(MOREX) +
aes(x = NEG2 , y = NEGn) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG3 , MOREX$NEG5)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG3 and MOREX$NEG5
## t = 16.557, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4116595 0.5085927
## sample estimates:
## cor
## 0.4615024ggplot(MOREX) +
aes(x = NEG3 , y = NEG5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG3 , MOREX$NEGn)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG3 and MOREX$NEGn
## t = 14.973, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3739587 0.4747923
## sample estimates:
## cor
## 0.4256961ggplot(MOREX) +
aes(x = NEG3 , y = NEGn) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$NEG5 , MOREX$NEGn)##
## Pearson's product-moment correlation
##
## data: MOREX$NEG5 and MOREX$NEGn
## t = 23.142, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5463134 0.6269246
## sample estimates:
## cor
## 0.5880775ggplot(MOREX) +
aes(x = NEG5 , y = NEGn) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
Correlation Between Sets of Items Representing the independent variables; RWA and SDO.
RWA
MOREX%>%
select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
lowerCor()%>%
corPlot()## rwa1 rwa2 rwa3 rwa4 rwa5 rwa6
## rwa1 1.00
## rwa2 0.29 1.00
## rwa3 0.26 0.70 1.00
## rwa4 0.37 0.25 0.25 1.00
## rwa5 0.52 0.34 0.32 0.50 1.00
## rwa6 0.23 0.27 0.28 0.30 0.32 1.00
cor.test(MOREX$rwa1 , MOREX$rwa2)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa1 and MOREX$rwa2
## t = 9.5866, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2309703 0.3438358
## sample estimates:
## cor
## 0.2884045ggplot(MOREX) +
aes(x = rwa1 , y = rwa2) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa1 , MOREX$rwa3)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa1 and MOREX$rwa3
## t = 8.5769, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2018975 0.3166612
## sample estimates:
## cor
## 0.2601981ggplot(MOREX) +
aes(x = rwa1 , y = rwa3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa1 , MOREX$rwa4)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa1 and MOREX$rwa4
## t = 12.676, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3156487 0.4219226
## sample estimates:
## cor
## 0.3699954ggplot(MOREX) +
aes(x = rwa1 , y = rwa4) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa1 , MOREX$rwa5)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa1 and MOREX$rwa5
## t = 19.472, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4756074 0.5652495
## sample estimates:
## cor
## 0.5218677ggplot(MOREX) +
aes(x = rwa1 , y = rwa5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa1 , MOREX$rwa6)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa1 and MOREX$rwa6
## t = 7.5028, df = 1013, p-value = 1.364e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1703141 0.2869248
## sample estimates:
## cor
## 0.2294426ggplot(MOREX) +
aes(x = rwa1 , y = rwa6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa2 , MOREX$rwa3)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa2 and MOREX$rwa3
## t = 31.434, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6701319 0.7325481
## sample estimates:
## cor
## 0.7026894ggplot(MOREX) +
aes(x = rwa2 , y = rwa3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa2 , MOREX$rwa4)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa2 and MOREX$rwa4
## t = 8.3439, df = 1013, p-value = 2.332e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1951019 0.3102820
## sample estimates:
## cor
## 0.2535906ggplot(MOREX) +
aes(x = rwa2 , y = rwa4) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa2 , MOREX$rwa5)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa2 and MOREX$rwa5
## t = 11.376, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2808427 0.3900157
## sample estimates:
## cor
## 0.3365597ggplot(MOREX) +
aes(x = rwa2 , y = rwa5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa2 , MOREX$rwa6)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa2 and MOREX$rwa6
## t = 8.8075, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2085925 0.3229358
## sample estimates:
## cor
## 0.2667024ggplot(MOREX) +
aes(x = rwa2 , y = rwa6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa3 , MOREX$rwa4)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa3 and MOREX$rwa4
## t = 8.0594, df = 1013, p-value = 2.146e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1867609 0.3024379
## sample estimates:
## cor
## 0.245473ggplot(MOREX) +
aes(x = rwa3 , y = rwa4) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa3 , MOREX$rwa5)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa3 and MOREX$rwa5
## t = 10.932, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2686818 0.3788056
## sample estimates:
## cor
## 0.3248443ggplot(MOREX) +
aes(x = rwa3 , y = rwa5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa3 , MOREX$rwa6)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa3 and MOREX$rwa6
## t = 9.4036, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2257492 0.3389694
## sample estimates:
## cor
## 0.2833463ggplot(MOREX) +
aes(x = rwa3 , y = rwa6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa4 , MOREX$rwa5)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa4 and MOREX$rwa5
## t = 18.529, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4556911 0.5476942
## sample estimates:
## cor
## 0.5031168ggplot(MOREX) +
aes(x = rwa4 , y = rwa5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa4 , MOREX$rwa6)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa4 and MOREX$rwa6
## t = 9.9565, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2414594 0.3535938
## sample estimates:
## cor
## 0.2985566ggplot(MOREX) +
aes(x = rwa4 , y = rwa6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$rwa5 , MOREX$rwa6)##
## Pearson's product-moment correlation
##
## data: MOREX$rwa5 and MOREX$rwa6
## t = 10.857, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2666184 0.3769003
## sample estimates:
## cor
## 0.3228548ggplot(MOREX) +
aes(x = rwa5 , y = rwa6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
SDO
MOREX%>%
select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
lowerCor()%>%
corPlot()## sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8
## sdo1 1.00
## sdo2 0.45 1.00
## sdo3 0.33 0.27 1.00
## sdo4 0.34 0.23 0.49 1.00
## sdo5 0.41 0.30 0.37 0.25 1.00
## sdo6 0.38 0.24 0.29 0.22 0.50 1.00
## sdo7 0.15 0.24 0.29 0.30 0.13 0.03 1.00
## sdo8 0.34 0.30 0.44 0.36 0.29 0.23 0.63 1.00
cor.test(MOREX$sdo1 , MOREX$sdo2)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo2
## t = 15.85, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3950757 0.4937614
## sample estimates:
## cor
## 0.445772ggplot(MOREX) +
aes(x = sdo1 , y = sdo2) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo1 , MOREX$sdo3)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo3
## t = 11.049, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2718931 0.3817689
## sample estimates:
## cor
## 0.3279396ggplot(MOREX) +
aes(x = sdo1 , y = sdo3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo1 , MOREX$sdo4)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo4
## t = 11.524, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2848930 0.3937422
## sample estimates:
## cor
## 0.3404578ggplot(MOREX) +
aes(x = sdo1 , y = sdo4) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo1 , MOREX$sdo5)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo5
## t = 14.153, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3536237 0.4564369
## sample estimates:
## cor
## 0.4063156ggplot(MOREX) +
aes(x = sdo1 , y = sdo5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo1 , MOREX$sdo6)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo6
## t = 12.886, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3211689 0.4269590
## sample estimates:
## cor
## 0.3752854ggplot(MOREX) +
aes(x = sdo1 , y = sdo6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo1 , MOREX$sdo7)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo7
## t = 4.8682, df = 1013, p-value = 1.306e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09050577 0.21076903
## sample estimates:
## cor
## 0.1511968ggplot(MOREX) +
aes(x = sdo1 , y = sdo7) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo1 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo1 and MOREX$sdo8
## t = 11.548, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2855367 0.3943341
## sample estimates:
## cor
## 0.3410771ggplot(MOREX) +
aes(x = sdo1 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo2 , MOREX$sdo3)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo2 and MOREX$sdo3
## t = 8.9112, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2115913 0.3257430
## sample estimates:
## cor
## 0.269614ggplot(MOREX) +
aes(x = sdo2 , y = sdo3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo2 , MOREX$sdo4)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo2 and MOREX$sdo4
## t = 7.5982, df = 1013, p-value = 6.817e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1731459 0.2896002
## sample estimates:
## cor
## 0.232205ggplot(MOREX) +
aes(x = sdo2 , y = sdo4) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo2 , MOREX$sdo5)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo2 and MOREX$sdo5
## t = 10.177, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2476687 0.3593589
## sample estimates:
## cor
## 0.3045604ggplot(MOREX) +
aes(x = sdo2 , y = sdo5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo2 , MOREX$sdo6)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo2 and MOREX$sdo6
## t = 7.8856, df = 1013, p-value = 8.069e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1816415 0.2976157
## sample estimates:
## cor
## 0.2404867ggplot(MOREX) +
aes(x = sdo2 , y = sdo6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo2 , MOREX$sdo7)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo2 and MOREX$sdo7
## t = 7.7433, df = 1013, p-value = 2.341e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1774408 0.2936545
## sample estimates:
## cor
## 0.2363929ggplot(MOREX) +
aes(x = sdo2 , y = sdo7) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo2 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo2 and MOREX$sdo8
## t = 9.9753, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2419912 0.3540879
## sample estimates:
## cor
## 0.299071ggplot(MOREX) +
aes(x = sdo2 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo3 , MOREX$sdo4)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo3 and MOREX$sdo4
## t = 17.707, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4377392 0.5318006
## sample estimates:
## cor
## 0.4861769ggplot(MOREX) +
aes(x = sdo3 , y = sdo4) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo3 , MOREX$sdo5)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo3 and MOREX$sdo5
## t = 12.528, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3117605 0.4183713
## sample estimates:
## cor
## 0.3662673ggplot(MOREX) +
aes(x = sdo3 , y = sdo5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo3 , MOREX$sdo6)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo3 and MOREX$sdo6
## t = 9.6274, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2321322 0.3449179
## sample estimates:
## cor
## 0.2895297ggplot(MOREX) +
aes(x = sdo3 , y = sdo6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo3 , MOREX$sdo7)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo3 and MOREX$sdo7
## t = 9.7292, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2350266 0.3476122
## sample estimates:
## cor
## 0.292332ggplot(MOREX) +
aes(x = sdo3 , y = sdo7) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo3 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo3 and MOREX$sdo8
## t = 15.414, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3846651 0.4844214
## sample estimates:
## cor
## 0.435881ggplot(MOREX) +
aes(x = sdo3 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo4 , MOREX$sdo5)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo4 and MOREX$sdo5
## t = 8.0835, df = 1013, p-value = 1.784e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1874673 0.3031028
## sample estimates:
## cor
## 0.2461608ggplot(MOREX) +
aes(x = sdo4 , y = sdo5) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo4 , MOREX$sdo6)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo4 and MOREX$sdo6
## t = 7.2145, df = 1013, p-value = 1.059e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1617338 0.2788073
## sample estimates:
## cor
## 0.2210668ggplot(MOREX) +
aes(x = sdo4 , y = sdo6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo4 , MOREX$sdo7)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo4 and MOREX$sdo7
## t = 9.9259, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2405971 0.3527925
## sample estimates:
## cor
## 0.2977225ggplot(MOREX) +
aes(x = sdo4 , y = sdo7) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo4 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo4 and MOREX$sdo8
## t = 12.188, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3027417 0.4101213
## sample estimates:
## cor
## 0.3576129ggplot(MOREX) +
aes(x = sdo4 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo5 , MOREX$sdo6)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo5 and MOREX$sdo6
## t = 18.491, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4548779 0.5469757
## sample estimates:
## cor
## 0.5023502ggplot(MOREX) +
aes(x = sdo5 , y = sdo6) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo5 , MOREX$sdo7)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo5 and MOREX$sdo7
## t = 4.018, df = 1013, p-value = 6.303e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06421093 0.18535374
## sample estimates:
## cor
## 0.1252492ggplot(MOREX) +
aes(x = sdo5 , y = sdo7) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo5 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo5 and MOREX$sdo8
## t = 9.6628, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2331376 0.3458540
## sample estimates:
## cor
## 0.2905032ggplot(MOREX) +
aes(x = sdo5 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo6 , MOREX$sdo7)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo6 and MOREX$sdo7
## t = 0.85744, df = 1013, p-value = 0.3914
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03466024 0.08831705
## sample estimates:
## cor
## 0.0269303ggplot(MOREX) +
aes(x = sdo6 , y = sdo7) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo6 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo6 and MOREX$sdo8
## t = 7.6406, df = 1013, p-value = 4.996e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1744026 0.2907870
## sample estimates:
## cor
## 0.2334306ggplot(MOREX) +
aes(x = sdo6 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$sdo7 , MOREX$sdo8)##
## Pearson's product-moment correlation
##
## data: MOREX$sdo7 and MOREX$sdo8
## t = 26.129, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5962743 0.6699034
## sample estimates:
## cor
## 0.6345263ggplot(MOREX) +
aes(x = sdo7 , y = sdo8) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
Correlation Between Items Representing the mediating variable; MEX.
MOREX%>%
select(MEX1, MEX2, MEX3)%>%
lowerCor()%>%
corPlot()## MEX1 MEX2 MEX3
## MEX1 1.00
## MEX2 0.46 1.00
## MEX3 0.40 0.76 1.00
cor.test(MOREX$MEX1 , MOREX$MEX2)##
## Pearson's product-moment correlation
##
## data: MOREX$MEX1 and MOREX$MEX2
## t = 16.66, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4140449 0.5107213
## sample estimates:
## cor
## 0.4637625ggplot(MOREX) +
aes(x = MEX1 , y = MEX2) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$MEX1 , MOREX$MEX3)##
## Pearson's product-moment correlation
##
## data: MOREX$MEX1 and MOREX$MEX3
## t = 13.686, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3417952 0.4457194
## sample estimates:
## cor
## 0.3950203ggplot(MOREX) +
aes(x = MEX1 , y = MEX3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
cor.test(MOREX$MEX2 , MOREX$MEX3)##
## Pearson's product-moment correlation
##
## data: MOREX$MEX2 and MOREX$MEX3
## t = 37.317, df = 1013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7336640 0.7856021
## sample estimates:
## cor
## 0.7608489ggplot(MOREX) +
aes(x = MEX2 , y = MEX3) +
geom_point(position = "jitter") +
geom_smooth(method="lm")
Exploratory Factor Analyses
By running exploratory factor analyses, we decided to parcel the questions of SDO scale in a following manner: SDOI as sdo1 with sdo2, SDOII as sdo3 with sdo4, SDOIII as sdo5 with sdo6, and SDOIV as sdo7 and sdo8. Moreover, the RWA scale was divided into two disticnt scales. RWAMIN containing rwa2 and rwa3 representing items addressing prejudice against the minority groups. And GENRWA containing the rest of the items representing general right wing athoritarian propensity.
SDO Scale
MOREX%>%
select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
factanal(factors=1, scores="regression")##
## Call:
## factanal(x = ., factors = 1, scores = "regression")
##
## Uniquenesses:
## sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8
## 0.644 0.746 0.582 0.676 0.693 0.777 0.766 0.558
##
## Loadings:
## Factor1
## sdo1 0.596
## sdo2 0.504
## sdo3 0.646
## sdo4 0.569
## sdo5 0.555
## sdo6 0.472
## sdo7 0.484
## sdo8 0.665
##
## Factor1
## SS loadings 2.558
## Proportion Var 0.320
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 644.06 on 20 degrees of freedom.
## The p-value is 1.47e-123MOREX%>%
select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
factanal(factors=2, scores="regression")##
## Call:
## factanal(x = ., factors = 2, scores = "regression")
##
## Uniquenesses:
## sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8
## 0.583 0.746 0.629 0.719 0.537 0.580 0.307 0.344
##
## Loadings:
## Factor1 Factor2
## sdo1 0.617 0.192
## sdo2 0.435 0.254
## sdo3 0.480 0.375
## sdo4 0.390 0.359
## sdo5 0.670 0.118
## sdo6 0.648
## sdo7 0.832
## sdo8 0.307 0.750
##
## Factor1 Factor2
## SS loadings 1.915 1.640
## Proportion Var 0.239 0.205
## Cumulative Var 0.239 0.444
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 170.64 on 13 degrees of freedom.
## The p-value is 1.37e-29MOREX%>%
select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
factanal(factors=3, scores="regression")##
## Call:
## factanal(x = ., factors = 3, scores = "regression")
##
## Uniquenesses:
## sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8
## 0.601 0.752 0.610 0.005 0.498 0.552 0.351 0.301
##
## Loadings:
## Factor1 Factor2 Factor3
## sdo1 0.576 0.166 0.199
## sdo2 0.424 0.239 0.103
## sdo3 0.404 0.316 0.356
## sdo4 0.204 0.200 0.956
## sdo5 0.695 0.102
## sdo6 0.663
## sdo7 0.793 0.141
## sdo8 0.305 0.764 0.149
##
## Factor1 Factor2 Factor3
## SS loadings 1.734 1.447 1.148
## Proportion Var 0.217 0.181 0.144
## Cumulative Var 0.217 0.398 0.541
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 81.78 on 7 degrees of freedom.
## The p-value is 5.97e-15MOREX%>%
select(sdo1, sdo2, sdo3, sdo4, sdo5, sdo6, sdo7, sdo8)%>%
factanal(factors=4, scores="regression")##
## Call:
## factanal(x = ., factors = 4, scores = "regression")
##
## Uniquenesses:
## sdo1 sdo2 sdo3 sdo4 sdo5 sdo6 sdo7 sdo8
## 0.024 0.740 0.005 0.697 0.498 0.481 0.119 0.436
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## sdo1 0.150 0.932 0.276
## sdo2 0.220 0.139 0.361 0.249
## sdo3 0.189 0.948 0.116 0.219
## sdo4 0.260 0.394 0.226 0.170
## sdo5 0.114 0.190 0.205 0.641
## sdo6 0.120 0.177 0.688
## sdo7 0.928 0.123
## sdo8 0.643 0.259 0.199 0.211
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 1.448 1.228 1.181 1.144
## Proportion Var 0.181 0.153 0.148 0.143
## Cumulative Var 0.181 0.334 0.482 0.625
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 3.52 on 2 degrees of freedom.
## The p-value is 0.172RWA Scale
MOREX%>%
select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
factanal(factors=1, scores="regression")##
## Call:
## factanal(x = ., factors = 1, scores = "regression")
##
## Uniquenesses:
## rwa1 rwa2 rwa3 rwa4 rwa5 rwa6
## 0.797 0.376 0.392 0.814 0.717 0.837
##
## Loadings:
## Factor1
## rwa1 0.451
## rwa2 0.790
## rwa3 0.780
## rwa4 0.431
## rwa5 0.532
## rwa6 0.404
##
## Factor1
## SS loadings 2.068
## Proportion Var 0.345
##
## Test of the hypothesis that 1 factor is sufficient.
## The chi square statistic is 466.74 on 9 degrees of freedom.
## The p-value is 7.55e-95MOREX%>%
select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
factanal(factors=2, scores="regression")##
## Call:
## factanal(x = ., factors = 2, scores = "regression")
##
## Uniquenesses:
## rwa1 rwa2 rwa3 rwa4 rwa5 rwa6
## 0.611 0.380 0.195 0.624 0.318 0.814
##
## Loadings:
## Factor1 Factor2
## rwa1 0.601 0.166
## rwa2 0.251 0.746
## rwa3 0.200 0.874
## rwa4 0.595 0.145
## rwa5 0.805 0.185
## rwa6 0.357 0.241
##
## Factor1 Factor2
## SS loadings 1.594 1.463
## Proportion Var 0.266 0.244
## Cumulative Var 0.266 0.509
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 8.68 on 4 degrees of freedom.
## The p-value is 0.0697MOREX%>%
select(rwa1, rwa2, rwa3, rwa4, rwa5, rwa6)%>%
factanal(factors=3, scores="regression")##
## Call:
## factanal(x = ., factors = 3, scores = "regression")
##
## Uniquenesses:
## rwa1 rwa2 rwa3 rwa4 rwa5 rwa6
## 0.400 0.378 0.196 0.527 0.410 0.786
##
## Loadings:
## Factor1 Factor2 Factor3
## rwa1 0.150 0.307 0.695
## rwa2 0.744 0.204 0.164
## rwa3 0.870 0.190 0.103
## rwa4 0.116 0.640 0.224
## rwa5 0.191 0.595 0.446
## rwa6 0.228 0.387 0.110
##
## Factor1 Factor2 Factor3
## SS loadings 1.434 1.086 0.782
## Proportion Var 0.239 0.181 0.130
## Cumulative Var 0.239 0.420 0.550
##
## The degrees of freedom for the model is 0 and the fit was 0Hypothesis 1
It was hypothesized that right-wing authoritarianism and social dominance orientation will predict negative intergroup attitudes against the Roma.
Confirmatory Factor Analysis
Confirmatory factor analysis was computed for the structural model. The errors of two pairs of items were allowed to covarry in order to improve the model fit. The model \(x^{2}\) of 407.497 showed no absolute fit (p < .001), while other measures confirmed that the model fit is overally satisfactory: CFI = .928; TLI = .908; SRMR = .053; and RMSEA = .063 and 90% CI = .057 - .069.
Model Specification
MORAL_MODEL1 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6"
FIT_MORAL_MODEL1 <- cfa(MORAL_MODEL1, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")Model Results and Plot
summary(FIT_MORAL_MODEL1, fit.measures=TRUE, standardized=TRUE)## lavaan (0.6-1) converged normally after 28 iterations
##
## Number of observations 1015
## Number of missing patterns 1
##
## Estimator ML Robust
## Model Fit Test Statistic 514.547 429.413
## Degrees of freedom 84 84
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.198
## for the Yuan-Bentler correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 4643.668 3640.092
## Degrees of freedom 105 105
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.905 0.902
## Tucker-Lewis Index (TLI) 0.881 0.878
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -29291.015 -29291.015
## Loglikelihood unrestricted model (H1) -29033.742 -29033.742
##
## Number of free parameters 51 51
## Akaike (AIC) 58684.030 58684.030
## Bayesian (BIC) 58935.085 58935.085
## Sample-size adjusted Bayesian (BIC) 58773.104 58773.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.071 0.064
## 90 Percent Confidence Interval 0.065 0.077 0.058 0.069
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.056 0.056
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## NEG1 1.365 0.069 19.766 0.000 1.365 0.677
## NEG2 1.562 0.070 22.295 0.000 1.562 0.660
## NEG3 1.194 0.068 17.581 0.000 1.194 0.586
## NEG5 1.459 0.062 23.471 0.000 1.459 0.729
## NEGn 1.268 0.062 20.569 0.000 1.268 0.694
## SDO =~
## SDOI 1.090 0.059 18.607 0.000 1.090 0.680
## SDOII 1.126 0.063 17.831 0.000 1.126 0.640
## SDOIII 1.091 0.066 16.494 0.000 1.091 0.574
## SDOIV 0.824 0.061 13.489 0.000 0.824 0.558
## MINRWA =~
## rwa2 1.815 0.063 28.890 0.000 1.815 0.799
## rwa3 1.656 0.056 29.333 0.000 1.656 0.880
## GENRWA =~
## rwa1 1.140 0.062 18.516 0.000 1.140 0.626
## rwa4 1.151 0.062 18.472 0.000 1.151 0.625
## rwa5 1.552 0.063 24.671 0.000 1.552 0.794
## rwa6 0.833 0.073 11.364 0.000 0.833 0.443
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## SDO 0.441 0.040 11.085 0.000 0.441 0.441
## MINRWA 0.499 0.035 14.390 0.000 0.499 0.499
## GENRWA 0.362 0.044 8.200 0.000 0.362 0.362
## SDO ~~
## MINRWA 0.425 0.039 10.834 0.000 0.425 0.425
## GENRWA 0.420 0.042 10.089 0.000 0.420 0.420
## MINRWA ~~
## GENRWA 0.506 0.038 13.284 0.000 0.506 0.506
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.730 0.063 43.118 0.000 2.730 1.353
## .NEG2 3.828 0.074 51.500 0.000 3.828 1.616
## .NEG3 3.866 0.064 60.407 0.000 3.866 1.896
## .NEG5 2.903 0.063 46.259 0.000 2.903 1.452
## .NEGn 2.725 0.057 47.490 0.000 2.725 1.491
## .SDOI 2.502 0.050 49.727 0.000 2.502 1.561
## .SDOII 2.795 0.055 50.560 0.000 2.795 1.587
## .SDOIII 3.838 0.060 64.377 0.000 3.838 2.021
## .SDOIV 2.151 0.046 46.426 0.000 2.151 1.457
## .rwa2 3.448 0.071 48.346 0.000 3.448 1.517
## .rwa3 2.809 0.059 47.537 0.000 2.809 1.492
## .rwa1 2.815 0.057 49.206 0.000 2.815 1.544
## .rwa4 2.556 0.058 44.211 0.000 2.556 1.388
## .rwa5 2.659 0.061 43.318 0.000 2.659 1.360
## .rwa6 2.507 0.059 42.466 0.000 2.507 1.333
## DISC 0.000 0.000 0.000
## SDO 0.000 0.000 0.000
## MINRWA 0.000 0.000 0.000
## GENRWA 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.206 0.161 13.726 0.000 2.206 0.542
## .NEG2 3.167 0.210 15.060 0.000 3.167 0.565
## .NEG3 2.731 0.164 16.697 0.000 2.731 0.657
## .NEG5 1.871 0.153 12.240 0.000 1.871 0.468
## .NEGn 1.734 0.120 14.409 0.000 1.734 0.519
## .SDOI 1.382 0.112 12.346 0.000 1.382 0.538
## .SDOII 1.832 0.148 12.392 0.000 1.832 0.591
## .SDOIII 2.418 0.140 17.317 0.000 2.418 0.670
## .SDOIV 1.501 0.125 12.052 0.000 1.501 0.689
## .rwa2 1.868 0.207 9.029 0.000 1.868 0.362
## .rwa3 0.802 0.154 5.213 0.000 0.802 0.226
## .rwa1 2.021 0.124 16.362 0.000 2.021 0.609
## .rwa4 2.067 0.171 12.104 0.000 2.067 0.609
## .rwa5 1.415 0.173 8.160 0.000 1.415 0.370
## .rwa6 2.844 0.159 17.867 0.000 2.844 0.804
## DISC 1.000 1.000 1.000
## SDO 1.000 1.000 1.000
## MINRWA 1.000 1.000 1.000
## GENRWA 1.000 1.000 1.000semPaths(FIT_MORAL_MODEL1, "Standardized", "Estimates")
Model Modification
modificationIndices(FIT_MORAL_MODEL1, sort.=TRUE, minimum.value=10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 105 NEG1 ~~ NEG2 83.969 1.008 1.008 0.382 0.382
## 144 NEG5 ~~ NEGn 64.304 0.685 0.685 0.380 0.380
## 69 DISC =~ rwa6 53.915 0.501 0.501 0.266 0.266
## 175 SDOII ~~ SDOIV 43.644 0.492 0.492 0.297 0.297
## 85 MINRWA =~ NEGn 36.730 -0.387 -0.387 -0.211 -0.211
## 182 SDOIII ~~ SDOIV 34.486 -0.458 -0.458 -0.240 -0.240
## 166 SDOI ~~ SDOIII 25.307 0.458 0.458 0.251 0.251
## 120 NEG2 ~~ NEG5 22.906 -0.525 -0.525 -0.216 -0.216
## 61 DISC =~ SDOII 22.515 -0.324 -0.324 -0.184 -0.184
## 93 MINRWA =~ rwa6 21.669 0.352 0.352 0.187 0.187
## 63 DISC =~ SDOIV 21.531 0.265 0.265 0.180 0.180
## 72 SDO =~ NEG3 20.315 0.348 0.348 0.171 0.171
## 68 DISC =~ rwa5 17.130 -0.293 -0.293 -0.150 -0.150
## 193 SDOIV ~~ rwa5 16.569 -0.253 -0.253 -0.174 -0.174
## 106 NEG1 ~~ NEG3 16.432 -0.388 -0.388 -0.158 -0.158
## 119 NEG2 ~~ NEG3 15.542 -0.445 -0.445 -0.151 -0.151
## 131 NEG2 ~~ rwa6 15.246 0.407 0.407 0.136 0.136
## 205 rwa1 ~~ rwa5 14.937 0.467 0.467 0.276 0.276
## 95 GENRWA =~ NEG2 14.273 0.297 0.297 0.126 0.126
## 98 GENRWA =~ NEGn 13.935 -0.223 -0.223 -0.122 -0.122
## 73 SDO =~ NEG5 13.595 -0.262 -0.262 -0.131 -0.131
## 83 MINRWA =~ NEG3 13.099 0.270 0.270 0.133 0.133
## 165 SDOI ~~ SDOII 13.003 -0.327 -0.327 -0.205 -0.205
## 107 NEG1 ~~ NEG5 12.262 -0.328 -0.328 -0.162 -0.162
## 126 NEG2 ~~ rwa2 11.886 0.334 0.334 0.137 0.137
## 67 DISC =~ rwa4 11.573 0.216 0.216 0.117 0.117
## 186 SDOIII ~~ rwa4 11.221 -0.272 -0.272 -0.122 -0.122
## 66 DISC =~ rwa1 11.118 -0.210 -0.210 -0.115 -0.115
## 80 SDO =~ rwa6 11.049 0.252 0.252 0.134 0.134
## 82 MINRWA =~ NEG2 10.933 0.277 0.277 0.117 0.117
## 164 NEGn ~~ rwa6 10.830 0.258 0.258 0.116 0.116
## 97 GENRWA =~ NEG5 10.820 -0.212 -0.212 -0.106 -0.106
## 159 NEGn ~~ rwa2 10.717 -0.239 -0.239 -0.133 -0.133
## 104 GENRWA =~ rwa3 10.298 -0.321 -0.321 -0.170 -0.170
## 103 GENRWA =~ rwa2 10.298 0.352 0.352 0.155 0.155
## 96 GENRWA =~ NEG3 10.159 0.224 0.224 0.110 0.110
## 116 NEG1 ~~ rwa4 10.131 0.250 0.250 0.117 0.117
## 148 NEG5 ~~ SDOIV 10.074 0.200 0.200 0.119 0.119Modified Model
MORAL_MODEL1 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
NEG5 ~~ NEGn
NEG2 ~~ NEG1"
FIT_MORAL_MODEL1 <- cfa(MORAL_MODEL1, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")Model Results and Plot
summary(FIT_MORAL_MODEL1, fit.measures=TRUE, standardized=TRUE)## lavaan (0.6-1) converged normally after 36 iterations
##
## Number of observations 1015
## Number of missing patterns 1
##
## Estimator ML Robust
## Model Fit Test Statistic 407.497 339.759
## Degrees of freedom 82 82
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.199
## for the Yuan-Bentler correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 4643.668 3640.092
## Degrees of freedom 105 105
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.928 0.927
## Tucker-Lewis Index (TLI) 0.908 0.907
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -29237.490 -29237.490
## Loglikelihood unrestricted model (H1) -29033.742 -29033.742
##
## Number of free parameters 53 53
## Akaike (AIC) 58580.981 58580.981
## Bayesian (BIC) 58841.881 58841.881
## Sample-size adjusted Bayesian (BIC) 58673.548 58673.548
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.063 0.056
## 90 Percent Confidence Interval 0.057 0.069 0.050 0.061
## P-value RMSEA <= 0.05 0.000 0.047
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053 0.053
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## NEG1 1.277 0.070 18.171 0.000 1.277 0.633
## NEG2 1.461 0.078 18.839 0.000 1.461 0.617
## NEG3 1.271 0.070 18.062 0.000 1.271 0.623
## NEG5 1.393 0.065 21.279 0.000 1.393 0.697
## NEGn 1.180 0.063 18.770 0.000 1.180 0.645
## SDO =~
## SDOI 1.089 0.058 18.645 0.000 1.089 0.679
## SDOII 1.127 0.063 17.910 0.000 1.127 0.640
## SDOIII 1.089 0.066 16.463 0.000 1.089 0.573
## SDOIV 0.825 0.061 13.518 0.000 0.825 0.559
## MINRWA =~
## rwa2 1.810 0.063 28.946 0.000 1.810 0.797
## rwa3 1.661 0.056 29.474 0.000 1.661 0.882
## GENRWA =~
## rwa1 1.140 0.062 18.531 0.000 1.140 0.626
## rwa4 1.151 0.062 18.518 0.000 1.151 0.625
## rwa5 1.553 0.063 24.744 0.000 1.553 0.794
## rwa6 0.833 0.073 11.386 0.000 0.833 0.443
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG5 ~~
## .NEGn 0.506 0.120 4.206 0.000 0.506 0.253
## .NEG1 ~~
## .NEG2 0.840 0.149 5.619 0.000 0.840 0.289
## DISC ~~
## SDO 0.471 0.041 11.424 0.000 0.471 0.471
## MINRWA 0.529 0.036 14.890 0.000 0.529 0.529
## GENRWA 0.383 0.046 8.371 0.000 0.383 0.383
## SDO ~~
## MINRWA 0.426 0.039 10.846 0.000 0.426 0.426
## GENRWA 0.420 0.042 10.080 0.000 0.420 0.420
## MINRWA ~~
## GENRWA 0.505 0.038 13.184 0.000 0.505 0.505
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.730 0.063 43.118 0.000 2.730 1.353
## .NEG2 3.828 0.074 51.500 0.000 3.828 1.616
## .NEG3 3.866 0.064 60.407 0.000 3.866 1.896
## .NEG5 2.903 0.063 46.259 0.000 2.903 1.452
## .NEGn 2.725 0.057 47.490 0.000 2.725 1.491
## .SDOI 2.502 0.050 49.727 0.000 2.502 1.561
## .SDOII 2.795 0.055 50.560 0.000 2.795 1.587
## .SDOIII 3.838 0.060 64.377 0.000 3.838 2.021
## .SDOIV 2.151 0.046 46.426 0.000 2.151 1.457
## .rwa2 3.448 0.071 48.346 0.000 3.448 1.517
## .rwa3 2.809 0.059 47.537 0.000 2.809 1.492
## .rwa1 2.815 0.057 49.206 0.000 2.815 1.544
## .rwa4 2.556 0.058 44.211 0.000 2.556 1.388
## .rwa5 2.659 0.061 43.318 0.000 2.659 1.360
## .rwa6 2.507 0.059 42.466 0.000 2.507 1.333
## DISC 0.000 0.000 0.000
## SDO 0.000 0.000 0.000
## MINRWA 0.000 0.000 0.000
## GENRWA 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.439 0.167 14.644 0.000 2.439 0.599
## .NEG2 3.472 0.224 15.484 0.000 3.472 0.619
## .NEG3 2.542 0.177 14.341 0.000 2.542 0.611
## .NEG5 2.058 0.160 12.855 0.000 2.058 0.515
## .NEGn 1.950 0.127 15.407 0.000 1.950 0.583
## .SDOI 1.384 0.112 12.375 0.000 1.384 0.538
## .SDOII 1.830 0.147 12.411 0.000 1.830 0.590
## .SDOIII 2.423 0.139 17.383 0.000 2.423 0.671
## .SDOIV 1.498 0.125 12.030 0.000 1.498 0.687
## .rwa2 1.887 0.205 9.189 0.000 1.887 0.365
## .rwa3 0.786 0.153 5.138 0.000 0.786 0.222
## .rwa1 2.021 0.123 16.378 0.000 2.021 0.609
## .rwa4 2.068 0.170 12.129 0.000 2.068 0.610
## .rwa5 1.412 0.173 8.150 0.000 1.412 0.369
## .rwa6 2.845 0.159 17.908 0.000 2.845 0.804
## DISC 1.000 1.000 1.000
## SDO 1.000 1.000 1.000
## MINRWA 1.000 1.000 1.000
## GENRWA 1.000 1.000 1.000semPaths(FIT_MORAL_MODEL1, "Standardized", "Estimates")
Structural Model
The path coefficients from MINRWA to discrimination (\(\beta\) = .37, p < .001), was significantly positive. The path coefficients from SDO to discrimination (\(\beta\) = .28, p < .001), was also significantly positive. No significant effect was found between GENRWA and discrimination.
MORAL_MODEL1 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
DISC ~ MINRWA + GENRWA + SDO
NEG2 ~~ NEG1
NEGn ~~ NEG5"
SEM_MORAL_MODEL1 <- sem(MORAL_MODEL1, data=MOREX, estimator = "MLR")Model Results and the Plot
summary(SEM_MORAL_MODEL1, standardized=TRUE)## lavaan (0.6-1) converged normally after 60 iterations
##
## Number of observations 1015
##
## Estimator ML Robust
## Model Fit Test Statistic 407.497 339.759
## Degrees of freedom 82 82
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.199
## for the Yuan-Bentler correction (Mplus variant)
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## NEG1 1.000 1.277 0.633
## NEG2 1.144 0.066 17.410 0.000 1.461 0.617
## NEG3 0.996 0.085 11.685 0.000 1.271 0.623
## NEG5 1.091 0.075 14.583 0.000 1.393 0.697
## NEGn 0.924 0.065 14.320 0.000 1.180 0.645
## SDO =~
## SDOI 1.000 1.089 0.679
## SDOII 1.035 0.087 11.836 0.000 1.127 0.640
## SDOIII 1.000 0.065 15.388 0.000 1.089 0.573
## SDOIV 0.758 0.076 9.974 0.000 0.825 0.559
## MINRWA =~
## rwa2 1.000 1.810 0.797
## rwa3 0.917 0.048 18.950 0.000 1.661 0.882
## GENRWA =~
## rwa1 1.000 1.140 0.626
## rwa4 1.009 0.073 13.920 0.000 1.151 0.625
## rwa5 1.362 0.079 17.287 0.000 1.553 0.794
## rwa6 0.730 0.080 9.104 0.000 0.833 0.443
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~
## MINRWA 0.261 0.040 6.515 0.000 0.370 0.370
## GENRWA 0.088 0.066 1.340 0.180 0.079 0.079
## SDO 0.328 0.060 5.445 0.000 0.280 0.280
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 ~~
## .NEG2 0.840 0.149 5.619 0.000 0.840 0.289
## .NEG5 ~~
## .NEGn 0.506 0.120 4.206 0.000 0.506 0.253
## SDO ~~
## MINRWA 0.839 0.089 9.412 0.000 0.426 0.426
## GENRWA 0.521 0.067 7.736 0.000 0.420 0.420
## MINRWA ~~
## GENRWA 1.043 0.111 9.371 0.000 0.505 0.505
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.439 0.167 14.644 0.000 2.439 0.599
## .NEG2 3.472 0.224 15.484 0.000 3.472 0.619
## .NEG3 2.542 0.177 14.342 0.000 2.542 0.611
## .NEG5 2.058 0.160 12.855 0.000 2.058 0.515
## .NEGn 1.950 0.127 15.407 0.000 1.950 0.583
## .SDOI 1.384 0.112 12.375 0.000 1.384 0.538
## .SDOII 1.830 0.147 12.411 0.000 1.830 0.590
## .SDOIII 2.423 0.139 17.383 0.000 2.423 0.671
## .SDOIV 1.498 0.125 12.030 0.000 1.498 0.687
## .rwa2 1.887 0.205 9.189 0.000 1.887 0.365
## .rwa3 0.786 0.153 5.138 0.000 0.786 0.222
## .rwa1 2.021 0.123 16.378 0.000 2.021 0.609
## .rwa4 2.068 0.170 12.129 0.000 2.068 0.610
## .rwa5 1.412 0.173 8.150 0.000 1.412 0.369
## .rwa6 2.845 0.159 17.908 0.000 2.845 0.804
## .DISC 1.047 0.126 8.298 0.000 0.642 0.642
## SDO 1.186 0.127 9.322 0.000 1.000 1.000
## MINRWA 3.277 0.226 14.473 0.000 1.000 1.000
## GENRWA 1.300 0.140 9.265 0.000 1.000 1.000semPaths(SEM_MORAL_MODEL1, "Standardized", "Estimates")
Hypothesis II
Next, we hypothesized that moral exclusion will mediate the relationship between endorsement of the two forms of prejudice and negative intergroup attitudes against the Roma.
Confirmatory Factor Analysis
Confirmatory factor analysis was computed for the structural model. The errors of two items were allowed to covarry in order to improve the model fit. The model \(x^{2}\) of 533.378 showed no absolute fit (p < .001), while other measures confirmed that the model fit is overally satisfactory: CFI = .930; TLI = .912; SRMR = .054; and RMSEA = .062 and 90% CI = .057 - .068.
Model Specification
MORAL_MODEL2 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
MEX =~ MEX2 + MEX3"
FIT_MORAL_MODEL2 <- cfa(MORAL_MODEL2, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")Model Results and Plot
summary(FIT_MORAL_MODEL2, fit.measures=TRUE, standardized=TRUE)## lavaan (0.6-1) converged normally after 30 iterations
##
## Number of observations 1015
## Number of missing patterns 1
##
## Estimator ML Robust
## Model Fit Test Statistic 608.250 513.420
## Degrees of freedom 109 109
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.185
## for the Yuan-Bentler correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 6245.860 5015.428
## Degrees of freedom 136 136
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.918 0.917
## Tucker-Lewis Index (TLI) 0.898 0.897
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -32774.717 -32774.717
## Loglikelihood unrestricted model (H1) -32470.592 -32470.592
##
## Number of free parameters 61 61
## Akaike (AIC) 65671.434 65671.434
## Bayesian (BIC) 65971.715 65971.715
## Sample-size adjusted Bayesian (BIC) 65777.974 65777.974
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067 0.060
## 90 Percent Confidence Interval 0.062 0.072 0.056 0.065
## P-value RMSEA <= 0.05 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054 0.054
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## NEG1 1.316 0.063 20.894 0.000 1.316 0.652
## NEG2 1.584 0.062 25.446 0.000 1.584 0.669
## NEG3 1.269 0.065 19.646 0.000 1.269 0.622
## NEG5 1.434 0.056 25.637 0.000 1.434 0.717
## NEGn 1.254 0.057 22.075 0.000 1.254 0.686
## SDO =~
## SDOI 1.090 0.058 18.695 0.000 1.090 0.680
## SDOII 1.125 0.063 17.840 0.000 1.125 0.639
## SDOIII 1.093 0.066 16.484 0.000 1.093 0.576
## SDOIV 0.823 0.061 13.527 0.000 0.823 0.558
## MINRWA =~
## rwa2 1.812 0.062 29.251 0.000 1.812 0.798
## rwa3 1.659 0.056 29.773 0.000 1.659 0.881
## GENRWA =~
## rwa1 1.141 0.062 18.548 0.000 1.141 0.626
## rwa4 1.154 0.062 18.517 0.000 1.154 0.626
## rwa5 1.548 0.063 24.516 0.000 1.548 0.792
## rwa6 0.835 0.073 11.389 0.000 0.835 0.444
## MEX =~
## MEX2 1.842 0.040 45.762 0.000 1.842 0.916
## MEX3 1.573 0.048 32.935 0.000 1.573 0.831
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC ~~
## SDO 0.446 0.040 11.296 0.000 0.446 0.446
## MINRWA 0.504 0.034 14.764 0.000 0.504 0.504
## GENRWA 0.367 0.044 8.428 0.000 0.367 0.367
## MEX 0.806 0.019 41.574 0.000 0.806 0.806
## SDO ~~
## MINRWA 0.426 0.039 10.841 0.000 0.426 0.426
## GENRWA 0.420 0.042 10.101 0.000 0.420 0.420
## MEX 0.322 0.037 8.773 0.000 0.322 0.322
## MINRWA ~~
## GENRWA 0.506 0.038 13.255 0.000 0.506 0.506
## MEX 0.381 0.033 11.562 0.000 0.381 0.381
## GENRWA ~~
## MEX 0.212 0.040 5.344 0.000 0.212 0.212
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.730 0.063 43.118 0.000 2.730 1.353
## .NEG2 3.828 0.074 51.500 0.000 3.828 1.616
## .NEG3 3.866 0.064 60.407 0.000 3.866 1.896
## .NEG5 2.903 0.063 46.259 0.000 2.903 1.452
## .NEGn 2.725 0.057 47.490 0.000 2.725 1.491
## .SDOI 2.502 0.050 49.727 0.000 2.502 1.561
## .SDOII 2.795 0.055 50.560 0.000 2.795 1.587
## .SDOIII 3.838 0.060 64.377 0.000 3.838 2.021
## .SDOIV 2.151 0.046 46.426 0.000 2.151 1.457
## .rwa2 3.448 0.071 48.346 0.000 3.448 1.517
## .rwa3 2.809 0.059 47.537 0.000 2.809 1.492
## .rwa1 2.815 0.057 49.206 0.000 2.815 1.544
## .rwa4 2.556 0.058 44.211 0.000 2.556 1.388
## .rwa5 2.659 0.061 43.318 0.000 2.659 1.360
## .rwa6 2.507 0.059 42.466 0.000 2.507 1.333
## .MEX2 4.655 0.063 73.726 0.000 4.655 2.314
## .MEX3 5.322 0.059 89.542 0.000 5.322 2.811
## DISC 0.000 0.000 0.000
## SDO 0.000 0.000 0.000
## MINRWA 0.000 0.000 0.000
## GENRWA 0.000 0.000 0.000
## MEX 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.337 0.144 16.275 0.000 2.337 0.574
## .NEG2 3.097 0.187 16.598 0.000 3.097 0.552
## .NEG3 2.547 0.161 15.870 0.000 2.547 0.613
## .NEG5 1.943 0.126 15.407 0.000 1.943 0.486
## .NEGn 1.770 0.107 16.556 0.000 1.770 0.530
## .SDOI 1.382 0.111 12.427 0.000 1.382 0.538
## .SDOII 1.835 0.148 12.431 0.000 1.835 0.592
## .SDOIII 2.413 0.140 17.254 0.000 2.413 0.669
## .SDOIV 1.502 0.124 12.064 0.000 1.502 0.689
## .rwa2 1.879 0.204 9.205 0.000 1.879 0.364
## .rwa3 0.793 0.150 5.274 0.000 0.793 0.224
## .rwa1 2.019 0.124 16.345 0.000 2.019 0.608
## .rwa4 2.061 0.171 12.066 0.000 2.061 0.608
## .rwa5 1.428 0.174 8.202 0.000 1.428 0.373
## .rwa6 2.841 0.159 17.831 0.000 2.841 0.803
## .MEX2 0.654 0.096 6.821 0.000 0.654 0.162
## .MEX3 1.110 0.089 12.492 0.000 1.110 0.310
## DISC 1.000 1.000 1.000
## SDO 1.000 1.000 1.000
## MINRWA 1.000 1.000 1.000
## GENRWA 1.000 1.000 1.000
## MEX 1.000 1.000 1.000semPaths(FIT_MORAL_MODEL2, "Standardized", "Estimates")
Model Modification
modificationIndices(FIT_MORAL_MODEL2, sort.=TRUE, minimum.value=10)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 140 NEG1 ~~ NEG2 77.767 0.894 0.894 0.332 0.332
## 185 NEG5 ~~ NEGn 60.807 0.585 0.585 0.315 0.315
## 81 DISC =~ rwa6 49.486 0.468 0.468 0.249 0.249
## 222 SDOII ~~ SDOIV 43.862 0.492 0.492 0.297 0.297
## 156 NEG2 ~~ NEG3 34.946 -0.615 -0.615 -0.219 -0.219
## 231 SDOIII ~~ SDOIV 34.939 -0.461 -0.461 -0.242 -0.242
## 101 MINRWA =~ NEGn 34.035 -0.369 -0.369 -0.202 -0.202
## 139 MEX =~ rwa6 27.748 0.318 0.318 0.169 0.169
## 211 SDOI ~~ SDOIII 24.725 0.453 0.453 0.248 0.248
## 73 DISC =~ SDOII 23.242 -0.322 -0.322 -0.183 -0.183
## 109 MINRWA =~ rwa6 21.372 0.349 0.349 0.185 0.185
## 141 NEG1 ~~ NEG3 19.685 -0.397 -0.397 -0.163 -0.163
## 127 MEX =~ NEG3 19.665 0.563 0.563 0.276 0.276
## 75 DISC =~ SDOIV 19.645 0.247 0.247 0.168 0.168
## 170 NEG2 ~~ MEX3 18.554 0.319 0.319 0.172 0.172
## 244 SDOIV ~~ rwa5 16.659 -0.254 -0.254 -0.173 -0.173
## 157 NEG2 ~~ NEG5 16.591 -0.398 -0.398 -0.162 -0.162
## 131 MEX =~ SDOII 15.665 -0.233 -0.233 -0.132 -0.132
## 209 NEGn ~~ MEX3 15.641 -0.224 -0.224 -0.160 -0.160
## 262 rwa1 ~~ rwa5 15.533 0.472 0.472 0.278 0.278
## 123 GENRWA =~ MEX2 15.507 -0.220 -0.220 -0.110 -0.110
## 124 GENRWA =~ MEX3 15.507 0.188 0.188 0.099 0.099
## 168 NEG2 ~~ rwa6 15.182 0.397 0.397 0.134 0.134
## 86 SDO =~ NEG3 14.060 0.283 0.283 0.139 0.139
## 125 MEX =~ NEG1 14.034 -0.467 -0.467 -0.231 -0.231
## 83 DISC =~ MEX3 13.555 1.495 1.495 0.789 0.789
## 82 DISC =~ MEX2 13.553 -1.750 -1.750 -0.870 -0.870
## 111 MINRWA =~ MEX3 13.539 0.189 0.189 0.100 0.100
## 110 MINRWA =~ MEX2 13.539 -0.221 -0.221 -0.110 -0.110
## 116 GENRWA =~ NEGn 13.086 -0.215 -0.215 -0.118 -0.118
## 197 NEG5 ~~ MEX3 12.605 -0.216 -0.216 -0.147 -0.147
## 210 SDOI ~~ SDOII 12.432 -0.319 -0.319 -0.200 -0.200
## 80 DISC =~ rwa5 12.287 -0.239 -0.239 -0.122 -0.122
## 163 NEG2 ~~ rwa2 11.969 0.327 0.327 0.136 0.136
## 113 GENRWA =~ NEG2 11.634 0.265 0.265 0.112 0.112
## 87 SDO =~ NEG5 11.600 -0.240 -0.240 -0.120 -0.120
## 78 DISC =~ rwa1 11.523 -0.208 -0.208 -0.114 -0.114
## 133 MEX =~ SDOIV 11.037 0.164 0.164 0.111 0.111
## 207 NEGn ~~ rwa6 10.978 0.257 0.257 0.115 0.115
## 235 SDOIII ~~ rwa4 10.973 -0.269 -0.269 -0.121 -0.121
## 94 SDO =~ rwa6 10.944 0.251 0.251 0.133 0.133
## 151 NEG1 ~~ rwa4 10.872 0.260 0.260 0.118 0.118
## 121 GENRWA =~ rwa2 10.487 0.346 0.346 0.152 0.152
## 122 GENRWA =~ rwa3 10.487 -0.317 -0.317 -0.168 -0.168
## 236 SDOIII ~~ rwa5 10.205 0.254 0.254 0.137 0.137
## 189 NEG5 ~~ SDOIV 10.051 0.198 0.198 0.116 0.116Modified Model
MORAL_MODEL2 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
MEX =~ MEX2 + MEX3
NEG2 ~~ NEG1"
FIT_MORAL_MODEL2 <- cfa(MORAL_MODEL2, data=MOREX, std.lv=TRUE,missing="fiml", estimator="MLR")Model Results and Plot
summary(FIT_MORAL_MODEL2, fit.measures=TRUE, standardized=TRUE)## lavaan (0.6-1) converged normally after 33 iterations
##
## Number of observations 1015
## Number of missing patterns 1
##
## Estimator ML Robust
## Model Fit Test Statistic 533.378 450.842
## Degrees of freedom 108 108
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.183
## for the Yuan-Bentler correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 6245.860 5015.428
## Degrees of freedom 136 136
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.930 0.930
## Tucker-Lewis Index (TLI) 0.912 0.912
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -32737.281 -32737.281
## Loglikelihood unrestricted model (H1) -32470.592 -32470.592
##
## Number of free parameters 62 62
## Akaike (AIC) 65598.562 65598.562
## Bayesian (BIC) 65903.766 65903.766
## Sample-size adjusted Bayesian (BIC) 65706.849 65706.849
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062 0.056
## 90 Percent Confidence Interval 0.057 0.068 0.051 0.061
## P-value RMSEA <= 0.05 0.000 0.023
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054 0.054
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Robust.huber.white
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## DISC =~
## NEG1 1.227 0.063 19.488 0.000 1.227 0.608
## NEG2 1.481 0.066 22.367 0.000 1.481 0.625
## NEG3 1.305 0.063 20.573 0.000 1.305 0.640
## NEG5 1.457 0.057 25.397 0.000 1.457 0.729
## NEGn 1.272 0.059 21.507 0.000 1.272 0.696
## SDO =~
## SDOI 1.087 0.058 18.678 0.000 1.087 0.678
## SDOII 1.127 0.063 17.924 0.000 1.127 0.640
## SDOIII 1.092 0.067 16.357 0.000 1.092 0.575
## SDOIV 0.826 0.061 13.519 0.000 0.826 0.559
## MINRWA =~
## rwa2 1.806 0.062 28.996 0.000 1.806 0.795
## rwa3 1.664 0.056 29.669 0.000 1.664 0.884
## GENRWA =~
## rwa1 1.142 0.061 18.577 0.000 1.142 0.626
## rwa4 1.151 0.062 18.498 0.000 1.151 0.625
## rwa5 1.552 0.063 24.715 0.000 1.552 0.794
## rwa6 0.832 0.073 11.367 0.000 0.832 0.442
## MEX =~
## MEX2 1.852 0.040 45.959 0.000 1.852 0.921
## MEX3 1.565 0.048 32.509 0.000 1.565 0.826
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 ~~
## .NEG2 0.888 0.125 7.094 0.000 0.888 0.300
## DISC ~~
## SDO 0.445 0.041 10.871 0.000 0.445 0.445
## MINRWA 0.496 0.035 13.982 0.000 0.496 0.496
## GENRWA 0.355 0.045 7.970 0.000 0.355 0.355
## MEX 0.812 0.021 39.059 0.000 0.812 0.812
## SDO ~~
## MINRWA 0.426 0.039 10.859 0.000 0.426 0.426
## GENRWA 0.420 0.042 10.081 0.000 0.420 0.420
## MEX 0.321 0.037 8.719 0.000 0.321 0.321
## MINRWA ~~
## GENRWA 0.505 0.038 13.123 0.000 0.505 0.505
## MEX 0.379 0.033 11.444 0.000 0.379 0.379
## GENRWA ~~
## MEX 0.209 0.040 5.265 0.000 0.209 0.209
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.730 0.063 43.118 0.000 2.730 1.353
## .NEG2 3.828 0.074 51.500 0.000 3.828 1.616
## .NEG3 3.866 0.064 60.407 0.000 3.866 1.896
## .NEG5 2.903 0.063 46.259 0.000 2.903 1.452
## .NEGn 2.725 0.057 47.490 0.000 2.725 1.491
## .SDOI 2.502 0.050 49.727 0.000 2.502 1.561
## .SDOII 2.795 0.055 50.560 0.000 2.795 1.587
## .SDOIII 3.838 0.060 64.377 0.000 3.838 2.021
## .SDOIV 2.151 0.046 46.426 0.000 2.151 1.457
## .rwa2 3.448 0.071 48.346 0.000 3.448 1.517
## .rwa3 2.809 0.059 47.537 0.000 2.809 1.492
## .rwa1 2.815 0.057 49.206 0.000 2.815 1.544
## .rwa4 2.556 0.058 44.211 0.000 2.556 1.388
## .rwa5 2.659 0.061 43.318 0.000 2.659 1.360
## .rwa6 2.507 0.059 42.466 0.000 2.507 1.333
## .MEX2 4.655 0.063 73.726 0.000 4.655 2.314
## .MEX3 5.322 0.059 89.542 0.000 5.322 2.811
## DISC 0.000 0.000 0.000
## SDO 0.000 0.000 0.000
## MINRWA 0.000 0.000 0.000
## GENRWA 0.000 0.000 0.000
## MEX 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .NEG1 2.564 0.146 17.500 0.000 2.564 0.630
## .NEG2 3.413 0.190 17.970 0.000 3.413 0.609
## .NEG3 2.455 0.160 15.326 0.000 2.455 0.591
## .NEG5 1.875 0.131 14.356 0.000 1.875 0.469
## .NEGn 1.724 0.113 15.233 0.000 1.724 0.516
## .SDOI 1.388 0.111 12.484 0.000 1.388 0.540
## .SDOII 1.831 0.147 12.428 0.000 1.831 0.590
## .SDOIII 2.416 0.141 17.194 0.000 2.416 0.670
## .SDOIV 1.498 0.125 12.028 0.000 1.498 0.687
## .rwa2 1.902 0.205 9.274 0.000 1.902 0.368
## .rwa3 0.774 0.152 5.080 0.000 0.774 0.218
## .rwa1 2.018 0.123 16.349 0.000 2.018 0.608
## .rwa4 2.068 0.171 12.123 0.000 2.068 0.610
## .rwa5 1.415 0.174 8.147 0.000 1.415 0.370
## .rwa6 2.847 0.159 17.901 0.000 2.847 0.804
## .MEX2 0.615 0.097 6.308 0.000 0.615 0.152
## .MEX3 1.138 0.090 12.636 0.000 1.138 0.317
## DISC 1.000 1.000 1.000
## SDO 1.000 1.000 1.000
## MINRWA 1.000 1.000 1.000
## GENRWA 1.000 1.000 1.000
## MEX 1.000 1.000 1.000semPaths(FIT_MORAL_MODEL2, "Standardized", "Estimates")
Structural Model
To investigate whether moral exclusion mediates the relation between SDO, MINRWA, as well as GENRWA and discrimination a path model was tested. using bootstrapped standard errors of 5000, the results indicated that, there is a significant indirect effect between SDO and discrimination through moral exclusion, \(\beta\) = .143 , SE = .032, p < .001, 95% CI[.079, .205]. The path model also showed a significant direct effect, \(\beta\) = .126, SE = .042, p = .002, 95% CI[.086, .408]. Hence a partial mediation was found.
A bootstrap estimation approach with 5000 samples was used which indicated that both direct \(\beta\) = .143 , SE = .032, p < .001, 95% CI[.079, .205] and indirect \(\beta\) = .217 , SE = .032, p < .001, 95% CI[.156, .282] effects were also significant for the relationship between MINRWA and discrimination through the mediating effect of moral exclusion. Thus a partial mediation was again detected.
Although GENRWA significantly predicted discrimination \(\beta\) = .089, p = .044, no significant mediational effect was found between GENRWA and discriminatory attitudes through moral exclusion.
MORAL_MODEL2 <- "
DISC =~ NEG1 + NEG2 + NEG3 + NEG5 + NEGn
SDO =~ SDOI + SDOII + SDOIII + SDOIV
MINRWA =~ rwa2 + rwa3
GENRWA =~ rwa1 + rwa4 + rwa5 + rwa6
MEX =~ MEX2 + MEX3
NEG2 ~~ NEG1
DISC ~ c1*SDO + c2*MINRWA + c3*GENRWA
MEX ~ a1*SDO + a2*MINRWA + a3*GENRWA
DISC ~ b*MEX
ab1 := a1*b
ab2 := a2*b
ab3 := a3*b
total1 := c1 + (a1 * b)
total2 := c2 + (a2 * b)
total3 := c3 + (a3 * b)"
SEM_MORAL_MODEL2 <- sem(MORAL_MODEL2, data=MOREX, se = "bootstrap", bootstrap = 5000)Model Results and the Plot
summary(SEM_MORAL_MODEL2, standardized=TRUE, ci = TRUE)## lavaan (0.6-1) converged normally after 68 iterations
##
## Number of observations 1015
##
## Estimator ML
## Model Fit Test Statistic 533.378
## Degrees of freedom 108
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Information Observed
## Observed information based on Hessian
## Standard Errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## DISC =~
## NEG1 1.000 1.000 1.000
## NEG2 1.207 0.064 18.742 0.000 1.094 1.345
## NEG3 1.063 0.077 13.777 0.000 0.926 1.229
## NEG5 1.188 0.070 17.022 0.000 1.064 1.338
## NEGn 1.037 0.065 16.012 0.000 0.923 1.175
## SDO =~
## SDOI 1.000 1.000 1.000
## SDOII 1.037 0.091 11.441 0.000 0.876 1.232
## SDOIII 1.004 0.066 15.159 0.000 0.886 1.146
## SDOIV 0.760 0.079 9.615 0.000 0.617 0.927
## MINRWA =~
## rwa2 1.000 1.000 1.000
## rwa3 0.922 0.049 18.777 0.000 0.833 1.024
## GENRWA =~
## rwa1 1.000 1.000 1.000
## rwa4 1.008 0.074 13.571 0.000 0.870 1.162
## rwa5 1.360 0.081 16.777 0.000 1.220 1.537
## rwa6 0.729 0.081 9.045 0.000 0.581 0.898
## MEX =~
## MEX2 1.000 1.000 1.000
## MEX3 0.845 0.026 32.309 0.000 0.794 0.896
## Std.lv Std.all
##
## 1.227 0.608
## 1.481 0.625
## 1.305 0.640
## 1.457 0.729
## 1.272 0.696
##
## 1.087 0.678
## 1.127 0.640
## 1.092 0.575
## 0.826 0.559
##
## 1.806 0.795
## 1.664 0.884
##
## 1.142 0.626
## 1.151 0.625
## 1.552 0.794
## 0.832 0.442
##
## 1.852 0.921
## 1.565 0.826
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## DISC ~
## SDO (c1) 0.143 0.048 2.971 0.003 0.052 0.240
## MINRWA (c2) 0.089 0.031 2.840 0.005 0.026 0.149
## GENRWA (c3) 0.096 0.049 1.950 0.051 0.005 0.197
## MEX ~
## SDO (a1) 0.346 0.078 4.457 0.000 0.193 0.498
## MINRWA (a2) 0.316 0.046 6.942 0.000 0.228 0.406
## GENRWA (a3) -0.052 0.082 -0.633 0.527 -0.216 0.107
## DISC ~
## MEX (b) 0.465 0.028 16.414 0.000 0.410 0.520
## Std.lv Std.all
##
## 0.126 0.126
## 0.131 0.131
## 0.089 0.089
##
## 0.203 0.203
## 0.308 0.308
## -0.032 -0.032
##
## 0.703 0.703
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .NEG1 ~~
## .NEG2 0.888 0.124 7.141 0.000 0.650 1.137
## SDO ~~
## MINRWA 0.836 0.089 9.434 0.000 0.659 1.010
## GENRWA 0.521 0.068 7.710 0.000 0.389 0.654
## MINRWA ~~
## GENRWA 1.040 0.112 9.310 0.000 0.829 1.266
## Std.lv Std.all
##
## 0.888 0.300
##
## 0.426 0.426
## 0.420 0.420
##
## 0.505 0.505
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .NEG1 2.564 0.146 17.516 0.000 2.276 2.847
## .NEG2 3.413 0.188 18.166 0.000 3.040 3.779
## .NEG3 2.455 0.159 15.448 0.000 2.140 2.762
## .NEG5 1.875 0.131 14.311 0.000 1.617 2.124
## .NEGn 1.724 0.114 15.128 0.000 1.496 1.944
## .SDOI 1.388 0.111 12.483 0.000 1.184 1.609
## .SDOII 1.831 0.148 12.332 0.000 1.534 2.121
## .SDOIII 2.416 0.142 16.958 0.000 2.125 2.692
## .SDOIV 1.498 0.124 12.033 0.000 1.252 1.744
## .rwa2 1.902 0.205 9.294 0.000 1.504 2.301
## .rwa3 0.774 0.155 4.985 0.000 0.461 1.075
## .rwa1 2.018 0.124 16.231 0.000 1.764 2.256
## .rwa4 2.068 0.170 12.159 0.000 1.735 2.399
## .rwa5 1.415 0.175 8.089 0.000 1.077 1.758
## .rwa6 2.847 0.159 17.875 0.000 2.525 3.151
## .MEX2 0.615 0.099 6.222 0.000 0.419 0.806
## .MEX3 1.138 0.091 12.471 0.000 0.964 1.321
## .DISC 0.416 0.067 6.229 0.000 0.285 0.548
## SDO 1.182 0.128 9.199 0.000 0.931 1.437
## MINRWA 3.262 0.227 14.379 0.000 2.820 3.711
## GENRWA 1.303 0.140 9.304 0.000 1.039 1.582
## .MEX 2.831 0.159 17.762 0.000 2.507 3.135
## Std.lv Std.all
## 2.564 0.630
## 3.413 0.609
## 2.455 0.591
## 1.875 0.469
## 1.724 0.516
## 1.388 0.540
## 1.831 0.590
## 2.416 0.670
## 1.498 0.687
## 1.902 0.368
## 0.774 0.218
## 2.018 0.608
## 2.068 0.610
## 1.415 0.370
## 2.847 0.804
## 0.615 0.152
## 1.138 0.317
## 0.277 0.277
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.825 0.825
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ab1 0.161 0.037 4.372 0.000 0.088 0.235
## ab2 0.147 0.023 6.396 0.000 0.102 0.194
## ab3 -0.024 0.038 -0.634 0.526 -0.100 0.050
## total1 0.304 0.059 5.164 0.000 0.192 0.420
## total2 0.236 0.038 6.212 0.000 0.161 0.311
## total3 0.072 0.063 1.134 0.257 -0.047 0.204
## Std.lv Std.all
## 0.143 0.143
## 0.217 0.217
## -0.022 -0.022
## 0.269 0.269
## 0.348 0.348
## 0.067 0.067parameterEstimates(SEM_MORAL_MODEL2, ci = TRUE, boot.ci.type = "norm", level = 0.95, standardized = TRUE)## lhs op rhs label est se z pvalue ci.lower ci.upper
## 1 DISC =~ NEG1 1.000 0.000 NA NA 1.000 1.000
## 2 DISC =~ NEG2 1.207 0.064 18.742 0.000 1.078 1.330
## 3 DISC =~ NEG3 1.063 0.077 13.777 0.000 0.908 1.210
## 4 DISC =~ NEG5 1.188 0.070 17.022 0.000 1.047 1.321
## 5 DISC =~ NEGn 1.037 0.065 16.012 0.000 0.907 1.160
## 6 SDO =~ SDOI 1.000 0.000 NA NA 1.000 1.000
## 7 SDO =~ SDOII 1.037 0.091 11.441 0.000 0.852 1.208
## 8 SDO =~ SDOIII 1.004 0.066 15.159 0.000 0.871 1.131
## 9 SDO =~ SDOIV 0.760 0.079 9.615 0.000 0.599 0.909
## 10 MINRWA =~ rwa2 1.000 0.000 NA NA 1.000 1.000
## 11 MINRWA =~ rwa3 0.922 0.049 18.777 0.000 0.823 1.015
## 12 GENRWA =~ rwa1 1.000 0.000 NA NA 1.000 1.000
## 13 GENRWA =~ rwa4 1.008 0.074 13.571 0.000 0.860 1.151
## 14 GENRWA =~ rwa5 1.360 0.081 16.777 0.000 1.196 1.514
## 15 GENRWA =~ rwa6 0.729 0.081 9.045 0.000 0.567 0.883
## 16 MEX =~ MEX2 1.000 0.000 NA NA 1.000 1.000
## 17 MEX =~ MEX3 0.845 0.026 32.309 0.000 0.794 0.896
## 18 NEG1 ~~ NEG2 0.888 0.124 7.141 0.000 0.646 1.133
## 19 DISC ~ SDO c1 0.143 0.048 2.971 0.003 0.048 0.236
## 20 DISC ~ MINRWA c2 0.089 0.031 2.840 0.005 0.029 0.152
## 21 DISC ~ GENRWA c3 0.096 0.049 1.950 0.051 -0.003 0.190
## 22 MEX ~ SDO a1 0.346 0.078 4.457 0.000 0.193 0.498
## 23 MEX ~ MINRWA a2 0.316 0.046 6.942 0.000 0.227 0.406
## 24 MEX ~ GENRWA a3 -0.052 0.082 -0.633 0.527 -0.212 0.110
## 25 DISC ~ MEX b 0.465 0.028 16.414 0.000 0.411 0.522
## 26 NEG1 ~~ NEG1 2.564 0.146 17.516 0.000 2.283 2.857
## 27 NEG2 ~~ NEG2 3.413 0.188 18.166 0.000 3.048 3.785
## 28 NEG3 ~~ NEG3 2.455 0.159 15.448 0.000 2.150 2.773
## 29 NEG5 ~~ NEG5 1.875 0.131 14.311 0.000 1.624 2.138
## 30 NEGn ~~ NEGn 1.724 0.114 15.128 0.000 1.507 1.954
## 31 SDOI ~~ SDOI 1.388 0.111 12.483 0.000 1.171 1.607
## 32 SDOII ~~ SDOII 1.831 0.148 12.332 0.000 1.547 2.129
## 33 SDOIII ~~ SDOIII 2.416 0.142 16.958 0.000 2.142 2.701
## 34 SDOIV ~~ SDOIV 1.498 0.124 12.033 0.000 1.258 1.746
## 35 rwa2 ~~ rwa2 1.902 0.205 9.294 0.000 1.504 2.306
## 36 rwa3 ~~ rwa3 0.774 0.155 4.985 0.000 0.478 1.086
## 37 rwa1 ~~ rwa1 2.018 0.124 16.231 0.000 1.779 2.266
## 38 rwa4 ~~ rwa4 2.068 0.170 12.159 0.000 1.738 2.404
## 39 rwa5 ~~ rwa5 1.415 0.175 8.089 0.000 1.080 1.765
## 40 rwa6 ~~ rwa6 2.847 0.159 17.875 0.000 2.545 3.169
## 41 MEX2 ~~ MEX2 0.615 0.099 6.222 0.000 0.425 0.812
## 42 MEX3 ~~ MEX3 1.138 0.091 12.471 0.000 0.959 1.317
## 43 DISC ~~ DISC 0.416 0.067 6.229 0.000 0.291 0.553
## 44 SDO ~~ SDO 1.182 0.128 9.199 0.000 0.932 1.436
## 45 MINRWA ~~ MINRWA 3.262 0.227 14.379 0.000 2.820 3.710
## 46 GENRWA ~~ GENRWA 1.303 0.140 9.304 0.000 1.029 1.578
## 47 MEX ~~ MEX 2.831 0.159 17.762 0.000 2.529 3.154
## 48 SDO ~~ MINRWA 0.836 0.089 9.434 0.000 0.667 1.015
## 49 SDO ~~ GENRWA 0.521 0.068 7.710 0.000 0.391 0.656
## 50 MINRWA ~~ GENRWA 1.040 0.112 9.310 0.000 0.821 1.259
## 51 ab1 := a1*b ab1 0.161 0.037 4.372 0.000 0.089 0.233
## 52 ab2 := a2*b ab2 0.147 0.023 6.396 0.000 0.102 0.193
## 53 ab3 := a3*b ab3 -0.024 0.038 -0.634 0.526 -0.099 0.051
## 54 total1 := c1+(a1*b) total1 0.304 0.059 5.164 0.000 0.188 0.418
## 55 total2 := c2+(a2*b) total2 0.236 0.038 6.212 0.000 0.163 0.312
## 56 total3 := c3+(a3*b) total3 0.072 0.063 1.134 0.257 -0.055 0.194
## std.lv std.all std.nox
## 1 1.227 0.608 0.608
## 2 1.481 0.625 0.625
## 3 1.305 0.640 0.640
## 4 1.457 0.729 0.729
## 5 1.272 0.696 0.696
## 6 1.087 0.678 0.678
## 7 1.127 0.640 0.640
## 8 1.092 0.575 0.575
## 9 0.826 0.559 0.559
## 10 1.806 0.795 0.795
## 11 1.664 0.884 0.884
## 12 1.142 0.626 0.626
## 13 1.151 0.625 0.625
## 14 1.552 0.794 0.794
## 15 0.832 0.442 0.442
## 16 1.852 0.921 0.921
## 17 1.565 0.826 0.826
## 18 0.888 0.300 0.300
## 19 0.126 0.126 0.126
## 20 0.131 0.131 0.131
## 21 0.089 0.089 0.089
## 22 0.203 0.203 0.203
## 23 0.308 0.308 0.308
## 24 -0.032 -0.032 -0.032
## 25 0.703 0.703 0.703
## 26 2.564 0.630 0.630
## 27 3.413 0.609 0.609
## 28 2.455 0.591 0.591
## 29 1.875 0.469 0.469
## 30 1.724 0.516 0.516
## 31 1.388 0.540 0.540
## 32 1.831 0.590 0.590
## 33 2.416 0.670 0.670
## 34 1.498 0.687 0.687
## 35 1.902 0.368 0.368
## 36 0.774 0.218 0.218
## 37 2.018 0.608 0.608
## 38 2.068 0.610 0.610
## 39 1.415 0.370 0.370
## 40 2.847 0.804 0.804
## 41 0.615 0.152 0.152
## 42 1.138 0.317 0.317
## 43 0.277 0.277 0.277
## 44 1.000 1.000 1.000
## 45 1.000 1.000 1.000
## 46 1.000 1.000 1.000
## 47 0.825 0.825 0.825
## 48 0.426 0.426 0.426
## 49 0.420 0.420 0.420
## 50 0.505 0.505 0.505
## 51 0.143 0.143 0.143
## 52 0.217 0.217 0.217
## 53 -0.022 -0.022 -0.022
## 54 0.269 0.269 0.269
## 55 0.348 0.348 0.348
## 56 0.067 0.067 0.067semPaths(SEM_MORAL_MODEL2, "Standardized", "Estimates")
Conclusion
Taken together, the first model showed that social dominance orientation as well as those items of the RWA scale addressing minority issues significantly predict discriminatory attitudes against the Roma community in Hungary. Such effect was supported in the second model as well, which also showed that moral exclusion partially explains the mechanism.